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The relative pose estimation of an inactive target by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, i.e. the rendezvous with a space debris and/or in-orbit servicing. Pose estimation systems based solely on a monocular camera are recently becoming an attractive alternative to systems based on active sensors or stereo cameras, due to their reduced mass, power consumption and system complexity. In this framework, a review of the robustness and applicability of monocular systems for the pose estimation of an uncooperative spacecraft is provided. Special focus is put on the advantages of multispectral monocular systems as well as on the improved robustness of novel image processing schemes and pose estimation solvers. The limitations and drawbacks of the validation of current pose estimation schemes with synthetic images are further discussed, together with the critical trade-offs for the selection of visual-based navigation filters. The state-of-the-art techniques are analyzed in order to provide an insight into the limitations involved under adverse illumination and orbit scenarios, high image contrast, background noise, and low signal-to-noise ratio, which characterize actual space imagery, and which could jeopardize the image processing algorithms and affect the pose estimation accuracy as well as the navigation filter's robustness. Specifically, a comparative assessment of current solutions is given at different levels of the pose estimation process, in order to bring a novel and broad perspective as compared to previous works.
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Review of the Robustness and Applicability of Monocular
Pose Estimation Systems for Relative Navigation with an
Uncooperative Spacecraft
Lorenzo Pasqualetto Cassinisa,
, Robert Fonoda, Eberhard Gilla
aDelft University of Technology, Kluyverweg 1 2629 HS, Delft, The Netherlands
Abstract
The relative pose estimation of an inactive target by an active servicer spacecraft is
a critical task in the design of current and planned space missions, due to its rele-
vance for close-proximity operations, i.e. the rendezvous with a space debris and/or
in-orbit servicing. Pose estimation systems based solely on a monocular camera
are recently becoming an attractive alternative to systems based on active sensors
or stereo cameras, due to their reduced mass, power consumption and system com-
plexity. In this framework, a review of the robustness and applicability of monocular
systems for the pose estimation of an uncooperative spacecraft is provided. Special
focus is put on the advantages of multispectral monocular systems as well as on
the improved robustness of novel image processing schemes and pose estimation
solvers. The limitations and drawbacks of the validation of current pose estima-
tion schemes with synthetic images are further discussed, together with the critical
trade-offs for the selection of visual-based navigation filters. The state-of-the-art
techniques are analyzed in order to provide an insight into the limitations involved
under adverse illumination and orbit scenarios, high image contrast, background
noise, and low signal-to-noise ratio, which characterize actual space imagery, and
which could jeopardize the image processing algorithms and affect the pose estima-
tion accuracy as well as the navigation filter’s robustness. Specifically, a comparative
Corresponding author
Email addresses: L.C.PasqualettoCassinis@tudelft.nl (Lorenzo Pasqualetto Cassinis),
R.Fonod@tudelft.nl (Robert Fonod), E.K.A.Gill@tudelft.nl (Eberhard Gill)
assessment of current solutions is given at different levels of the pose estimation
process, in order to bring a novel and broad perspective as compared to previous
works.
Keywords: Relative pose estimation, Active Debris Removal, In-Orbit Servicing,
Monocular cameras, Image Processing, Visual-based navigation filters
2010 MSC: 00-01, 99-00
List of Abbreviations
ADR Active Debris Removal
BRIEF Binary Robust Independent Elementary Features
CLAHE Contrast Limited Adaptive Histogram Equalization
CNN Convolutional Neural Network
DA Differential Algebra
DQ-MEKF Dual Quaternion Modified Extended Kalman Filter
DSS Distributed Space Systems
EDL Edge Drawing Lines
EO Electro-Optical
ESA European Space Agency
EKF Extended Kalman Filter
FF Formation Flying
FREAK Fast Retina Keypoint
GEO Geostationary Earth Orbit
GFTT Good Feature to Track
GNC Guidance, Navigation and Control
2
GNFIR Goddard Natural Feature Image Recognition
GPS Global Positioning System
HCD Harris Corner Detection
HEO High Elliptical Orbit
HIL Hardware-In-the-Loop
HST Hubble Space Telescope
HT Hough Transform
ICP Iterative Closest Point
IMU Inertial Measurement Unit
IOS In-Orbit Servicing
IoU Intersection-Over-Union
IP Image Processing
IRLS Iteratively Re-Weighted Least Squares
KF Kalman Filter
LEO Low Earth Orbit
LIDAR LIght Detection And Ranging
MEKF Multiplicative Extended Kalman Filter
MRP Modified Rodrigues Parameters
MSRN Multi-Spectral Sensing for Relative Navigation
MWIR Mid-Wave Infra-Red
LPF Low Pass Filter
LSD Line Segment Detector
3
LWIR Long-Wave Infra-Red
NIR Near Infra-Red
NRM Newton Raphson Method
RF Radio Frequency
PCA Principal Component Analysis
PC-P Phase Congruency Point
PnP Perspective-n-Point
RANSAC RANdom SAmple Consensus
ROE Relative Orbital Elements
ROI Region Of Interest
RCM Roberts Cross Method
RPN Region Proposal Network
SIL Sotware-In-the-Loop
SIFT Scale-Invariant Transform
ST Shi-Tomasi
SURF Speeded Up Robust Features
SNR Signal-To-Noise Ratio
TIR Thermal Infra-Red
TOF Time-Of-Flight
UKF Unscented Kalman Filter
VBS Visual-based System
VNIR Visual-Near Infra-Red
4
WGE Weak Gradient Elimination
1. Introduction
In the past years, advancements in the field of Distributed Space Systems (DSS)
have been made to cope with the increasing demand for robust and reliable engi-
neering solutions in challenging scenarios for Guidance, Navigation, and Control
(GNC), such as Formation Flying (FF) missions, In-Orbit Servicing (IOS), and Active5
Debris Removal (ADR).
Previous research in the context of FF has led to robust and reliable real-time
estimation of the position and velocity of a target object with respect to the main
spacecraft. Navigation architectures which combine absolute and relative measure-
ments have been designed and implemented in past and current missions that rely10
either on Radio Frequency (RF), Global Positioning System (GPS) sensors or on cam-
eras. As an example, the PRISMA mission provided the first in-orbit demonstration
of non-GPS RF-based metrology instruments for relative navigation [1], and recent
improvements have been made to use a Visual-Based System (VBS) as the main nav-
igation system in more recent missions [2]. Moreover, additional effort has been15
made in the recent years on IOS and assembly and Debris Removal [3, 4]. For these
close-proximity scenarios, the relative position and orientation, herewith referred
to as pose, of a target spacecraft with respect to a servicer spacecraft, represent a
key information for the navigation system. A proper characterization of the target
spacecraft is essential to determine its status and to plan the final strategy of the ap-20
proaching orbit during autonomous close-proximity operations. Notably, the pose
estimation problem is in this case complicated by the fact that the target satellite is,
especially in the context of ADR, uncooperative, namely retained as non functional
and/or not able to aid the relative navigation. In particular, the additional flexibility
required to deal with a non-functional and/or freely tumbling target has an impact25
on the navigation system. Compared to FF missions or more commonly to coop-
erative close-proximity missions, vision-based sensors should be preferred over RF
5
sensors when the satellite is uncooperative. Additionally, the navigation system can-
not rely on known visual markers installed on the target spacecraft, and requires ad-
vanced Image Processing (IP) and pose estimation algorithms in order to cope with30
the lack of knowledge of the initial relative position and attitude. Moreover, if the
target is tumbling at a relatively high rate, additional challenges arise in the tracking
of the relative pose due to the fast relative dynamics.
From a high-level perspective, visual-based sensors can be divided into active
and passive devices, depending on whether they require power to function, i.e. LIght35
Detection And Ranging (LIDAR) sensors and Time-Of-Flight (TOF) cameras, or if
they passively acquire radiation, i.e. monocular and stereo cameras. Spacecraft rel-
ative navigation usually exploit Electro-Optical (EO) sensors such as stereo cameras
[5, 6] and/or a LIDAR sensor [7] in combination with one or more monocular cam-
eras, in order to overcome the partial observability that results from the lack of range40
information in these latter [8]. However, systems based solely on monocular cam-
eras are currently being investigated given the fact that monocular navigation en-
sures rapid pose determination under low power and mass requirements [9], which
is an asset given the constraints in the processing power available for in-flight pose
estimation, while on the other hand, stereo cameras and LIDAR sensors are less flex-45
ible and less convenient in terms of operational range, mass, power consumption
and processing power [10]. The range unobservability problem of monocular cam-
eras can indeed be tackled if a wireframe 3D model of the target is included in the
pose estimation, by matching it with features extracted from the 2D monocular im-
age and solving for the full relative pose, or alternatively if an offline database of50
images of the target is available together with their associated pose label. However,
given the low Signal-To-Noise Ratio (SNR) and the high contrast which characterize
space images, a significant effort is still required to comply with most of the de-
manding requirements for a robust and accurate monocular-based navigation sys-
tem.55
In the presented framework, the aim of this paper is to provide a detailed overview
of the robustness and applicability of state-of-the-art monocular-based pose esti-
mation systems for the relative navigation with an uncooperative target. Recent
6
surveys on the topic focused on a comparative assessment of the pose estimation
solvers [11] or provided a broader review on cooperative as well as uncooperative60
targets by including monocular- as well as stereo- and LIDAR-based systems [10].
Furthermore, only monocular cameras operating in the visible spectrum where re-
viewed, and recent estimation methods based on deep learning techniques were
not included. The novelty of this work stands in extending the previous surveys in
mainly three directions. Firstly, focus is put on the applicability and robustness of65
multispectral monocular cameras. Secondly, both IP systems and pose estimation
algorithms are analyzed with particular emphasis on the relative range they were
tested on, the robustness with respect to the image background, and on the syn-
thetic and real images database adopted for their validation. Furthermore, novel
pose estimation schemes are reviewed which are based on Convolutional Neural70
Networks (CNN). Finally, a review is presented for the navigation filters currently
adopted. A distinction is made between known targets, for which mass and inertia
properties as well as a 3D model of the target are known and available, and partially
known targets, for which the uncertainty is constrained to the target center of mass
and moment of inertia, while a 3D model of the target is available. Notably, this dis-75
tinction impacts on the internal dynamics of the navigation filter rather than on the
image processing and pose estimation prior to the filter. The reader is referred to
Opromolla et al. [10] for an overview of the pose estimation of uncooperative un-
known targets, for which neither the target mass and inertia properties nor a 3D
model of the target are available prior to the on-line estimation.80
The paper is organized as follows. Section 2 presents a review of the robustness
and applicability of monocular cameras operating in the visible (VIS), Near Infrared
(NIR) and Mid/Long Wave Infrared (MWIR/LWIR), the latter type of cameras being
also referred to as Thermal Infrared (TIR) cameras. Section 3 contains a detailed re-
view of IP algorithms as well as pose estimation algorithms which have been devel-85
oped for uncooperative targets. Section 4 provides a review of visual-based naviga-
tion systems with focus on the navigation filters currently adopted. Finally, Section
5 lists the main conclusions and recommendations.
7
2. Review of Monocular EO Sensors
One of the first applications of VIS cameras for the pose estimation of an unco-90
operative target is represented by the Relative Navigation Sensor which flew as part
of the Hubble Space Telescope (HST) Servicing Mission 4 (SM4). The camera suite
consisted of three monocular cameras operating at long (28 m - 260 m), medium
(6 m - 40 m) and short (2 m - 5.5 m) range [12] to aid the estimation of pose of the
target telescope, assumed to be unknown. Subsequently, inspired by the promising95
applications of existing visual-based systems for present and future FF missions and
in-orbit servicing missions, many authors continued with the investigation of the
feasibility of VIS cameras for the pose estimation of uncooperative spacecraft. Du
et al. [13] proposed a scheme which combines a singular VIS camera, in the closing
(15 m - 300 m) and mid-range (5 m - 15 m) phases, with two collaborative monocular100
VIS cameras in the final approach phase (1 m - 5 m), in order to increase the camera
FoV and aid the feature extraction within the IP system. The cameras were used to
estimate the pose of large non-cooperative satellites in Geostationary Earth Orbit
(GEO). Liu and Hu [14] evaluated the performance of a pose estimation method for
cylinder-shaped spacecraft which makes use of single images from a monocular VIS105
camera, whereas D’Amico et al. [15], Sharma and D’Amico [16] and Sharma et al.
[9, 17] used images collected by the monocular VIS camera onboard the PRISMA
mission to investigate the robustness of several pose estimation schemes with re-
spect to image noise, illumination conditions and Earth in the background geome-
tries. Furthermore, Schnitzer et al. [18] included two monocular VIS cameras in the110
sensors suite adopted in their on-ground testing of image-based non-cooperative
rendezvous navigation, and Pesce et al. [19] adopted a single passive monocular
camera to reconstruct the pose of an uncooperative, known target. Despite the
differences in the experimental setup, as well as in the pose estimation schemes, a
common feature that was found for VIS cameras, even for cooperative pose estima-115
tion, is their strong dependency on the Solar or Earth illumination, which becomes
more severe when the target does not have any fiducial marker.
On the other hand, TIR cameras are infrared cameras sensitive to the mid- and
8
far-infrared spectral ranges (3 µm - 14 µm). Due to size, complexity, and power con-
sumption of cryogenically-cooled infrared sensors, the current state-of-the-art on120
TIR cameras for spacecraft relative navigation relies on uncooled microbolometers
operating in the range 8 µm - 14 µm, as they can provide sufficient sensitivity at
low cost [20]. This type of sensor was flight-tested as part of the LIRIS demonstrator
during the ATV5 Mission [21] as well as part of the Raven ISS Hosted Payload [22],
and it has been used in [23] as well as in [24] and in [18] to assess the robustness of125
a TIR-based navigation system for ADR and to validate a pose estimation method
based on feature extraction, respectively. Also, Shi et al. [25, 26, 27] used synthetic
and real TIR camera images to validate a model-based and an appearance-based
pose estimation methods, respectively. Notably, the TIR camera in [22] was fused
with a visual camera and a flash LIDAR in order to improve the overall sensors per-130
formance.
When compared to VIS cameras, TIR cameras do not depend on external light
sources but rather on the emitted thermal radiation of the target spacecraft, thus
avoiding any saturation due to Sun presence in the camera FoV or Earth in the back-
ground. This makes the sensor more robust against the different illumination con-135
ditions, typical of an ADR scenario [28]. On the other hand, their image resolution
is usually much lower than VIS camera. As reported in [23], the amount of blur in
the image can significantly affect the performance of feature detection algorithms
within the IP system. Also, the results of the tests with real TIR camera images in
[18], in which a scaled model of the Envisat was heated through resistors mounted140
on the rear of the plates and a Halogen lamp was used for the illumination, demon-
strated that real TIR images clearly differ from synthetic images. More in particular,
Barrel distorsion was found to be more severe than the one modelled in the syn-
thetic dataset, and the edges of the spacecraft silhouette were found more faded in
the real images compared to the synthetic ones. Furthermore, the different thermal145
dynamics encountered during an ADR mission due to varying temperature profile
of the target over one orbit, as well as the different thermal surface coatings of the
target, introduce some challenges in the imaging. As an example, the performance
of the method proposed in [25] cannot be evaluated due to the too optimistic as-
9
Table 1: Advantages and disadvantages of TIR/NIR/VIS cameras for space applications, based on the
reviewed papers. Here, the characteristics of VIS cameras are referred to as ’Nominal’ for clarity of the
comparison.
Saturation due
to the Sun
Robustness
w.r.t. Eclipse
Robustness w.r.t. Earth
in background Image quality
Robustness w.r.t
thermal dynamics
VIS Nominal Nominal Nominal Nominal Nominal
TIR Superior Superior Superior Inferior Inferior
NIR Nominal Superior Nominal Nominal Inferior
sumptions of the thermal environment of the target. Furthermore, as stated in [27],150
the resolution of TIR images sensibly affects the accuracy of the pose determination
in the training phase of a non-model based method.
Finally, NIR cameras are cameras which operate in the spectral range from 780 to
2500 nm. As such, current CMOS/CCD technologies can be adopted to sense the in-
coming NIR radiation, and a superior image quality compared to TIR microbolome-155
ters can be achieved. To the best of the authors’ knowledge, the only pose estima-
tion scheme so far tested with NIR images is based on a model-based IP in which the
camera suite combines VIS/NIR/TIR images to increase the robustness of the pose
estimation 1. This work was part of a Technology Research Programme (TRP) study,
sponsored by the European Space Agency (ESA) and called Multi-spectral Sensing160
for Relative Navigation (MSRN), which focused on the design of a multispectral cam-
era that can be used for navigation purposes in a wide variety of scenarios. This
activity focused on increasing the accuracy and robustness of normal multispec-
tral cameras by combining a Visual-Near Infra-Red (VNIR) spectral channel to a TIR
spectral channel [29]. In this way, the benefits of each single camera type, listed165
in Table 1, can be combined to return a superior performance of the camera suite.
Figure 1 illustrates the different coupling schemes proposed. Data fusion both at
1https://www.esa.int/Our_Activities/Space_Engineering_Technology/Shaping_the_
Future/Multispectral_Sensing_for_Relative_Navigation
10
image and image processing levels was investigated in order to comply with the re-
quirements of a robust and computationally fast IP prior to the navigation filter.
170
The current state of the art on monocular cameras is further reviewed by focus-
ing on the applicability of the proposed camera suites for the desired operational
range, considering the requirement to have a robust pose estimation of an uncoop-
erative target from several hundreds of meters down to docking, which characterises
most of the close-proximity rendezvous missions. Table 2 lists some relevant char-175
acteristics of the camera suites and reports the tested range of the pose estimation
simulations. Naasz et al. [12] and Cavrois et al. [21] tested monocular cameras down
to 0.5 meters from the target and down to actual docking, respectively. However,
the challenges of feature extraction within the IP at close range were not investi-
gated. As an example, with a FoV of around 23 degrees and a distance from the180
target of around 0.5 meters, the IP would need to extract features from a portion
of the spacecraft as small as a 0.2 m-by-0.2 m rectangle, which can be challenging
if the satellite is relatively large. On the other hand, the claim in [13] that collabo-
rative cameras are strictly required for the close approach phase relates to the fact
that their selected IP scheme is based on the extraction of large rectangular features185
of large communication GEO satellites. Other authors investigated several different
pose estimation schemes which rely on more flexible feature extractions. However,
their pose estimation systems were not tested for relative ranges below 5 meters. It
can be concluded that some effort is still required to assess whether a single monoc-
ular camera can be used for close-proximity pose estimation of an uncooperative190
target or if collaborative cameras are needed. As a general remark, it should in prin-
ciple be possible to rely on a single monocular camera when the target is fully in
the camera FoV, and switch to the feature tracking of the desired docking port for
closer ranges, as performed in [18]. Furthermore, several orbit scenarios should be
recreated in future tests in order to investigate the robustness and applicability of195
each type of monocular camera as well as a combined VNIR/TIR camera suite for
multispectral imaging. The scheme in Figure 1, as well as the one proposed in [22]
provided that no LIDAR systems are considered, shall be investigated. Finally, the
11
Figure 1: Illustration of the cameras coupling schemes investigated during ESAs MSRN programme. The
selected third scheme combines the advantages of relying on data fusion prior to the IP (scheme 1) with
the benefits of having separate channels, which improves the system robustness in case of failurein either
the VNIR or in the TIR band (scheme 2).
infrared characteristics of the target spacecraft should be fully understood in order
to maximize the performance of the NIR/TIR cameras. Although Yilmaz et al. [30]200
proposed an infrared signature estimation method capable of characterizing the dy-
namical thermal behaviour of space debris, some effort is still required to assess its
validity and to confirm whether an exact infrared appearance model of the target is
needed for a robust relative navigation solution which relies on IR images.
3. Monocular Pose Estimation205
Monocular pose estimation consists in estimating the relative pose of a target
spacecraft with respect to the servicer spacecraft by only using 2D images, either
taken by a monocular camera or fused from more monocular cameras (Figure 1),
as measurements. In other words, monocular pose estimation is associated to the
computation of pseudomeasurements of the relative pose from the input image, prior210
to the navigation filter. From a high level perspective, the architecture of the pose
estimation process usually involves an acquisition step, or initialization, in which
there is no a-priori information on the target pose, and a tracking step, in which
12
Table 2: Characteristics of the camera suites adopted in different pose estimation schemes and their
tested range.
Ref. Camera Suite Tested range FoV [deg]
[12] 3 monocular VIS cameras 150 m - 1 m 11/23/23
[13] monocular + collaborative
VIS cameras
300 m - 1 m 55
[14] Monocular VIS camera 40 m - 5 m -
[15, 16, 9, 17] Monocular VIS camera 13 m - 8 m 22.3 - 16.8
[21] 3 Monocular VIS/TIR cameras 70 km - 8 km
3.5 km - docking
60x45
[25, 27, 26] Monocular TIR camera 5 m 40
- Monocular VNIR/TIR camera1far range - 7 m 40x40 VNIR
40x30 TIR
[23] Monocular TIR camera - 30
[18] 2 Monocular VIS/TIR cameras 100 m - docking -
[24] Monocular TIR camera 70 m - 21 m -
[19] Monocular VIS camera < 30 m -
13
knowledge from the previous estimates is used when new images of the target are
acquired. In both cases, estimation methods can be divided into model-based and215
non-model based. Model-based pose estimation makes use of a simplified wire-
frame 3D model of the target and it is described in detail in Section 3.1. On the other
hand, non-model based methods estimate the spacecraft pose without using an ex-
isting 3D model of the target. In this review, appearance-based and feature-based
methods are considered. In appearance-based methods, the pose estimation is per-220
formed by comparing the 2D image with a pre-stored database of images and by
minimizing the matching error between the in-flight image and each of the images
in the database. As such, no feature extraction is required and thus no IP system is
needed. Appearance-based methods are reviewed in Section 3.2.
In addition to the above-mentioned methods, CNNs are recently becoming a225
promising solution for the pose initialization of a target spacecraft. In a CNN-based
method, the monocular image is fed into a pre-trained neural network, which solves
a regression and/or a classification problem to return the predicted pose. Depend-
ing on the selected architecture adopted to solve for the relative pose, these methods
can either rely on a wireframe 3D model of the target spacecraft or solely on the 2D230
images used in the training, and hence they can either be referred to as non-model
based or model-based. Figure 2 illustrates a high level representation of the monoc-
ular pose estimation methods reviewed in this paper. Feature-based methods are
included beside the other pose estimation methods to underline that the features
extracted by the IP algorithms could also represent input measurements for the nav-235
igation filter.
3.1. Model-based Pose Estimation
Model-based monocular pose estimation methods receive as input a 2D image
and match it with an existing wireframe 3D model of the target spacecraft to esti-
mate the pose of such target with respect to the servicer camera by extracting some240
features from the 2D image (IP system, described in Section 3.1.1) and by match-
ing these features to the corresponding elements of the 3D model. Then, the rela-
tive pose is obtained by solving the Perspective-n-Points (PnP) Problem described
14
Figure 2: High level architecture of monocular pose estimation methods reviewed in this paper.
in Section 3.1.2. Interested readers are referred to [10] for a more detailed overview
on template matching as an alternative to solving the PnP problem.245
3.1.1. IP Algorithms
The IP system is a fundamental step for feature-based pose estimation, and sev-
eral methods exist in literature to extract and detect target features from a monocu-
lar 2D image, based on the specific application. From a high-level perspective, the
target features can be divided into keypoints (or interest points), corners, edges and250
depth maps. Table 3 provides a list of the IP schemes reviewed in this Section.
Naasz et al. [12] accomodated two different IP within their Relative Navigation
Sensor (RNS) system: a Sobel edge-enhancing image filter to process a 10-bit cam-
era image and perform the edge extraction, also adopted in [22], and a digital corre-
lation image processing technique which computed the position of certain features255
of the target spacecraft. These two methods were used separately by different pose
estimation systems which were tested during the HST-SM4. Several realistic light-
15
Table 3: Characteristics of state-of-the-art IP algorithms. Here, NA refers to the fact that no robustness
test could be found in the reference. Notice that no information on the robustness is reported for TIR-
based systems, given the negligible Earth’s emittance in the TIR band.
Ref. IP Tested Range
Robust w.r.t.
Earth in the background
Offline Database
required
[12] Digital corr./
Sobel
150 m - 1 m NA No
[13] Canny + HT 300 m - 1 m NA No
[14] Ellipses extraction 40 m - 5 m NA No
[15] LPF + Canny + HT 13 m- 8 m NA No
[25] RCM + HCD 5m - No
[22] Sobel NA NA No
[26] CLAHE + SIFT/
BRIEF + RANSAC
- - Yes
[24] Canny 100 m - 21 m - Yes
[31] FREAK + EDL NA NA Yes
[9] WGE + S/HT 13 m - 8 m Robust when Earth’s horizon
is not in the FoV
No
[19] GFTT < 30 m NA No
[32] Prewitt + gradient filter
ST+HT+LSD
45 m - 5 m Robustness proven
(including Earth’s horizon in FoV)
No
16
ing conditions were recreated to validate the robustness of the IP algorithms with
respect to illumination. Du et al. [13] included a median filter before the other steps
of the IP to cope with image noise and smooth the data. The Canny edge detection260
algorithm was selected to detect edges in the image, and a subsequent Hough trans-
form (HT) [33] was used to extract the detected lines. Several tests were conducted
to assess the robustness of the IP with respect to image noise at different variance
levels. However, a limitation of their method was that it focused on the extraction
of rectangular structures on a large target spacecraft. Liu and Hu [14] presented a265
robust method based on ellipses extraction for cylinder-shaped spacecraft, but its
application is not feasible for the pose estimation of a spacecraft of generic shape.
D’Amico et al. [15] used the same feature detection and extraction methods in
[13] in combination with a Low-Pass Filter (LPF). Its method was tested with the
PRISMA image dataset and proved to be flexible with respect to the spacecraft shape,270
but it lacked of robustness to illumination and background conditions. Further-
more, it did not prove to be robust with respect to the spacecraft symmetry. Shi
et al. [25] selected the Roberts Cross Method (RCM) in combination with the Har-
ris Corner Detection (HCD) method to improve the computational time of the IP.
However, the limitations of the RCM in producing less edges than the Canny’s were275
not assessed. Shi et al. [26] implemented a Contrast Limited Adaptive Histogram
Equalization (CLAHE) to clean and restore blurred TIR images. A Scale Invariant
Feature Transform (SIFT) [34], in combination with the Binary Robust Independent
Elementary Features (BRIEF) method [35], was used to extract the target interest
points from the denoised image. The RANdom SAmple Consensus (RANSAC) [36]280
algorithm was further included in the IP scheme in order to quickly extract image
features and descriptors by using some internally pre-stored test image features for
feature matching.
Yilmaz et al. [37] performed an evaluation of the invariance of edge and corner
detectors applied to TIR images. The Good Feature to Track (GFTT), Speeded Up Ro-285
bust Features (SURF) and Phase Congruency Point (PC-P) edge algorithms, as well
as edge detectors such as the Sobel, were traded-off based on their robustness under
different thermal conditions representative of the dynamic space thermal environ-
17
ment. Their results showed that thermal variations can cause significant variation in
the thermal signatures, and thus challenge the robustness of pose estimation meth-290
ods based on feature extraction. Rondao et al. [38] also investigated the performance
of several keypoint detectors applied to VIS/TIR synthetic images. In their work, the
combination of the Fast-Hessian feature detector with the Binary Robust Invariant
Scalable Keypoints (BRISK) descriptor proved to have comparable performance in
both spectra, resulting in a promising option when reduced memory usage repre-295
sent a key requirement.
Gansmann et al. [24] adopted the Canny algorithm to extract edges from TIR im-
ages and from a 2D rendered representation of the target, obtained by projecting a
3D model. The variation in brightness and the variation in depth were used to ex-
tract the edges from the TIR images and from the render, respectively. Furthermore,300
Rondao and Aouf [31] adopted a Fast Retina Keypoint (FREAK) descriptor in com-
bination with the Edge Drawing Lines (EDL) detector to extract keypoints, corners,
and edges to find the correspondence between features. In their method, a depth
mapping was further performed which aided the features extraction. The limitation
of these two latter methods is that they require an offline database for image match-305
ing.
More recently, Sharma et al. [9] proposed a novel technique to eliminate the back-
ground of images, called Weak Gradient Elimination (WGE). After using a Gauss
filter to blur the original image and aid the feature extraction, the image gradient
intensities were computed, and the WGE was used to threshold the weak gradient310
intensities corresponding to the Earth in the background. In the next step, the Sobel
algorithm and the Hough Transform (S/HT ) were used to extract and detect fea-
tures. Notably, the WGE technique can also be used to identify a rectangular region
of interest (ROI) in the image which can allow an automated selection of the hyper-
parameters required by the HT. In this way, the hyperparameters are automatically315
scaled based on the varying distance from the target. By creating two parallel pro-
cessing flows, the method proved to be able to extract main body features as well
as particular structures such as antennas, and thus to solve the symmetry ambi-
guity which characterized other IP schemes. Furthermore, the implementation of
18
(a) (b) (c)
Figure 3: Examples of feature synthesis schemes. (a) [31], (b) [9], (c) [32].
the WGE method returned a much higher robustness with respect to Earth in the320
background compared to the other methods. However, scenarios in which the Earth
horizon is present in the background represented a challenge for the IP due to an
improper ROI detection.
Alternatively, Capuano et al. [32] introduced a new IP scheme in which three
different parallel processing streams, which use the Shi-Tommasi (ST) corners de-325
tector, the HT, and the Line Segment Detector (LSD), are exploited in order to fil-
ter three sets of points and improve the robustness of the feature detection. This
was performed in order to overcome the different drawbacks of each single method.
Feature fusion was then used to synthesise the detected points into polylines which
resemble parts of the spacecraft body. By including a background removal step sim-330
ilar to the WGE in [9], which makes use of a Prewitt operator in combination with
a gradient filter, the authors could also demonstrate the robustness of their IP with
respect to the Earth in the background. Furthermore, the scenarios with the Earth
horizon were tackled by tuning the threshold of gradient filter to a more selective
value. The last three feature extraction schemes [31, 9, 32], which combine several335
keypoints, edges and corners detectors, are depicted in Figure 3.
Finally, Pasqualetto et al. [39] investigated the potentials of using a hourglass
neural network [40] to extract the corners of a target spacecraft prior to the pose es-
timation. In this method, the output of neural network is a set of so called heatmaps
around the features used in the offline training. The coordinates of each heatmap’s340
19
peak intensity characterize the predicted feature location, with the intensity indicat-
ing the confidence of locating the corresponding keypoint at this position. Despite a
lack of actual space imagery to test the network performance, the proposed method
proved to be capable of detecting features which are either not visible due to adverse
illumination or occulted by other parts of the target, when trained and tested with345
synthetic images. Due to these characteristics, the proposed method could emerge
as a promising alternative to state-of-the-art IP algorithms. However, the robustness
of the features extraction with respect to the Earth in the background was not fully
proven, and the impact of an inaccurate detection on the pose estimation accuracy
was not assessed.350
As a general remark, IP algorithms based on keypoint features detectors present
some advantages compared to algorithms based on edge and corner detectors, given
their invariance to perspective, scale and illumination changes [34, 41]. However,
they could still be sensitive to extreme illumination scenarios. Moreover, their ro-355
bustness with respect to outliers, which would be present when the Earth is in the
image background, has not been fully proved yet in the framework of relative pose
estimation in space. On the other hand, the recent advancements in the IP algo-
rithms based on corners/edges detection showed an improvement in the robust-
ness of such methods with respect to the Earth in the background [9]. Furthermore,360
edges and corners detectors are retained to be more robust than features detectors
in case of partial occlusion of the target, especially during tracking [42]. Future
works should focus on the assessment of the robustness of keypoint features de-
tectors to outliers in space imagery, as well as in combining such IP methods with
edges/corners detectors in order to benefit from the advantages in both algorithms,365
similarly to what has been proposed in [31]. Moreover, more investigation should be
performed to assess the performance of feature detection methods based on neu-
ral networks, especially given their robustness with respect to adverse illumination
conditions and partial occultation of the target.
20
Figure 4: Schematic representation of the pose estimation problem using a monocular image [9].
3.1.2. Pose Estimation Methods370
The features detected by the IP algorithms described in Section 3.1.1 can be di-
rectly used as measurements in a navigation filter to solve for the pose of the target
spacecraft. This is usually performed when the extracted features are represented by
points. However, pseudomeasurements of the relative pose are usually computed
from the extracted features and a wireframe 3D model of the target by solving a pose375
initialization problem. Referring to Figure 4, the pose initialization problem consists
in determining the position of the target’s centre of mass tCand its orientation with
respect to the camera frame C, represented by the rotation matrix RC
B. The 3D/2D
true perspective equations,
rC=RC
BqB+tC, (1)
21
p=(ui,vi)=µxC
zCfx+Cx,yC
zCfy+Cy, (2)
relate the unknown pose with the corresponding point pin the image plane. Here,380
qBis a point in the 3D model, expressed in the body-frame coordinate system B,
whereas fxand fydenote the focal lengths of the camera and (Cx,Cy) are the princi-
pal points of the image. Since solving the PnP problem requires an image processing
suite that extracts target features from a given image, Eqn. 1 and 2 do not have to be
solved for non-model based estimators such as CNN-based or appearance-based.385
Several methods exist in the literature to solve for the initial pose of an uncoop-
erative target. Based on two different surveys by Opromolla et al. [10] and Sharma
and D’Amico [11], the most commonly used solvers can be identified as the PosIt
[43] and Coplanar PosIt [44], the SoftPOSIT [45], the EPnP [46] and the Newton390
Raphson Method (NRM). In [31], the EPnP solver was used to initialize the relative
pose, which was further refined by means of an M-Estimator minimization to in-
crease the robustness with respect to erroneous correspondences between features.
In their method, the Rodrigues parameters were used to represent the relative atti-
tude in order to handle a 6×1 pose vector. In a recent effort, Sharma et al. [9] further395
proved that the EPnP method has the highest success rate and offers a superior per-
formance in terms of both pose accuracy and runtime when compared with other
state-of-the-art PnP solvers. In their estimation scheme, the NRM was also used af-
ter the EPnP to refine the final pose estimation. The idea behind such PnP solver
switch is that, since EPnP has the lowest runtime, it can be used when large num-400
ber of correspondence hypotheses need to be validated within the first iterations.
Once the search space for correct feature correspondence has been reduced, NRM
can be used due to its better accuracy in the presence of outliers and noise [11]. Fur-
thermore, Pesce et al. [19] proposed a novel pose estimation scheme in which the
RANSAC algorithm is used in combination with the Principal Component Analy-405
sis (PCA) to generate subsets of image-model correspondences, so called consensus
sets. For this purpose, the features extracted with the GFTT algorithm were com-
22
pared with an off-line feature point classification of a simplified 3D model. Once
the correspondences are set, the EPnP is used to solve for the pose initialization.
The SoftPosIt algorithm was further included to solve for the pose tracking. Due to410
the capability to detect particular spacecraft components, their estimation scheme
proved to be robust with respect to spacecraft symmetry.
Aside from the listed solvers adopted to solve the pose initialization problem,
other authors [12, 22] implemented the technique proposed in [47] and the ULTOR
engine [48] in their Goddard Natural Feature Image Recognition (GNFIR) and UL-415
TOR algorithms, respectively, for the pose tracking. As opposed to PnP solvers, this
technique makes use of the Lie group SO(3) to find and measure the distance be-
tween a rendered model of the target and the matching nearby edges in the image.
In their works, the GNFIR algorithm was adopted to perform edge tracking once the
pose initialization is acquired, whereas ULTOR could be used for both pose initial-420
ization and tracking. Additionally, Gansmann et al. [24] assumed the initialization
to be known and implemented a tracking method based on [47] which uses an It-
eratively Re-Weighted Least Squares (IRLS) to get an a-posteriori pose via the inter-
frame motion. Their algorithm minimized the squared residuals of model template
edges, extracted from a 3D rendering of the target, to image query edges, extracted425
from each TIR image. Their tracking algorithm was tested for the distance of 100m
until 21m and proved to return centimetric and sub-degree accuracy for the rela-
tive pose. However, convergence to local minima associated to a wrong pose rep-
resented an issue with the algorithm. A proposed solution to this problem was to
perform a re-initialization of the pose estimation with an acquisition algorithm, as a430
sudden jump in the estimated pose would be easily detected due to the smoothness
of the relative motion.
The comparative assessment of the different PnP solvers in [11] is reported in
Table 4. Table 5 lists some characteristics of the different pose estimation solvers435
in relation to the IP methods described in Section 3.1.1. From the comparison, it
can be concluded that the pose estimation scheme proposed in [9] is a good can-
didate for the pose initialization, given the robustness of its IP system and the fact
23
Table 4: Comparative assessment results from simulations as a qualitative decision matrix in [11]. Here,
PosIt+ refers to a solver that can switch between Coplanar PosIt and PosIt.
Solver Number of Features Noise Outliers Distance to Camera
PosIt Nominal Superior Inferior Nominal
EPnP Superior Par Inferior Inferior
PosIt+ Nominal Superior Inferior Nominal
NRM Superior Superior Nominal Nominal
Figure 5: Novel pose determination subsystem proposed in [9].
that it has been tested for several illumination conditions as well as with the Earth
in the background. The proposed system is in fact robust to the background of the440
images due to the WGE, it requires no a-priori knowledge of the target spacecrafts
pose, and it is computationally efficient. In particular, this architecture shows im-
provements with respect to previous IP and pose estimation techniques [15, 11, 10].
Figure 5 illustrates the main steps of the pose determination subsystem. However,
some remarks shall be made about the images used for the validation of the pose445
estimation schemes. As reported in Table 4, most of the pose estimation schemes
were tested with synthetic images in which the different reflectivities of spacecraft
materials were not included. As such, the robustness of the algorithms with respect
to realistic illumination conditions could not be assessed. Also, the limited amount
of realistic space images available in [15], [24] and [9] could not represent all the450
challenging orbital scenarios for which a specific camera-target-Sun-Earth geome-
try would affect the pose estimation accuracy.
Following the recommendations in [18], real VIS/TIR/NIR images should be ac-
24
Table 5: Characteristics of state-of-the-art model-based pose estimation schemes. Here, NA refers to the
fact that no robustness tests could be found in the reference.
Ref. IP
Pose Initialization/
Tracking Tested Range
Robust w.r.t.
symmetry
Validation
Database
[12] Digital corr./
Sobel
ULTOR/
GNFIR
150 m - 1 m NA Flight spare cameras/
Lab pictures
[13] Canny + HT Analytical 300 m - 1 m NA
Synthetic images
Realistic camera model
No materials’ reflectivity
[14] Ellipses
extraction NRM 40 m - 5 m Yes
Synthetic images
Ideal camera model
No materials’ reflectivity
[15] LPF + Canny +
HT
Perceptual Groups
+ NRM
13 m- 8 m No Actual space imagery
(PRISMA)
[25] RCM + HCD SoftPosIt 5m NA
Synthetic images
Camera model not given
No materials’ reflectivity
[22] Sobel GNFIR NA - -
[26]
CLAHE +
SIFT/BRIEF +
RANSAC
EPnP/SoftPosit - NA
Synthetic and lab TIR images
Camera model not given
No materials’ reflectivity
[24] Canny IRLS 100 m - 21 m NA Actual space imagery
(ISS)
[31] FREAK + EDL EPnP/RANSAC +
M-estimator
NA Yes
Synthetic images
Camera model not given
Materials’ reflectivity included
[9] WGE + S/HT EPnP + NRM 13 m - 8 m Yes Actual space imagery
(PRISMA)
[19] GFTT RANSAC + PCA +
EPnP/SoftPosIt
< 30 m Yes -
25
counted for early in an activity to avoid validating navigation algorithms with syn-
thetic images which considerably differ from the ones taken in orbit. In the future,455
image acquisition tests should be conducted on ground with real cameras and S/C
mock-ups, in order to solve both the low representativeness of synthetic images
and the limited amount of actual space imagery. Furthermore, since the genera-
tion of representative TIR images in a laboratory environment requires the space-
craft model to have thermal signatures which are usually difficult to reproduce, an460
additional effort will be required in order to account for thermal effects as well as to
hide the image background. It is worth mentioning that, due to the fast variation in
the space thermal environment, a model-based method could be unfeasible when
using TIR images. As anticipated in [37] and [23], the different thermal inertia of
spacecraft materials could result in a mismatch between the off-line TIR model and465
the time-varying extracted features and could thus lead to inaccurate relative pose
estimates. An idea could be to adopt a model-based pose estimation which uses im-
ages from a VIS camera in combination with a non-model based method which uses
images from a TIR camera. In this way, the limited observability which results from
the TIR-based estimation could be solved, and both the robustness and the accuracy470
of the pose estimation improved.
3.2. Appearance-based Pose Estimation
Compared to feature-based methods, in which the IP is used to extract features
such as corners and edges, only the spacecraft appearance is used in appearance-
based methods. Depending on whether a 3D model of the target spacecraft is used475
or not, appearance-based methods can be classified as model-based and non-model
based, respectively. Opromolla et al. [49] proposed a model-based pose framework
for spacecraft pose estimation. However, the framework was designed to process
3D point clouds and thus its application was constrained to LIDARs or stereovision
systems. To the best of the author’s knowledge, the only appearance-based method480
for spacecraft pose estimation based on a monocular camera was proposed by Shi
et al. [27], and it is based on PCA.
The pose matching algorithm is separated into an off-line training portion and
26
a testing portion that computes the pose of the spacecraft in-flight. The PCA algo-
rithm matches the object from the camera image (test image) to a stored matrix of485
images that has been transformed to its eigenspaces during the training phase. The
advantage of PCA stands in the fact that the dimension of the training dataset can
be drastically reduced by considering only the principal eigenvectors of the training
data matrix. However, the test image needs to be compared to each image of the
training dataset at each pose solution, which still requires a considerable computa-490
tional effort if the number of training frames is large. In [27], the validation of the
algorithm was performed with M=12.660 frames as a result of a trade-off between
the computational time and the estimation accuracy. The resulting mean search
time was found to be approximately 62.8 ms, which is relatively low for uncoopera-
tive pose estimation.495
However, the PCA algorithm performance was proved to degrade with the image
noise, which is unwanted due to the noisyness of actual space imagery. Further-
more, one of the assumptions for the PCA is that the object must be completely
visible, which might not be the case if part of the spacecraft falls outside the camera
FoV. Finally, as the validation was not performed with the Earth in the background,500
it is unclear whether the pose estimation is robust against other objects present in
the camera image, as one of the main requirements of PCA is that each image shall
contain a single, non-occulted object.
3.3. CNN-based Pose Estimation
From a high-level perspective, CNNs are neural networks built from multiple505
dual-layers of convolutional masks which were inspired by the human visual cor-
tex. Given their capability of classifying images, their implementation in monocu-
lar pose estimation has become attractive in recent years [50]. A pose estimation
architecture based on CNNs does not distinguish between an IP subsystem and a
pose estimation subsystem, but rather between an off-line training phase and an510
in-flight test phase. The advantage of CNNs over feature-based algorithms is an in-
crease in the robustness for adverse illumination condition, as well as a reduction in
the computational complexity. However, compared to terrestrial applications, space
27
imagery are characterized by high contrast, low signal-to-noise-ratio and low sensor
resolution. As such, their accuracy is expected to be lower. Usually, due to the lack515
of a large synthetic dataset of space images, which is usually required to fully train
a CNN, a network which has been pretrained on a dataset of terrestrial images is
used, and transfer learning is applied to train only a limited number of layers of the
convolutional network.
A CNN architecture for pose estimation for uncooperative spacecraft has been520
proposed in [17]. Synthetic datasets of up to 125.000 space images were created, for
which a 3D texture model of the target spacecraft was required. The architecture of
the AlexNet network [51] was then adopted as the baseline architecture, and a clas-
sification problem was solved to return the relative pose of the target spacecraft as-
sociated to each image. Transfer learning was used to train the last fully-connected525
layers using a subset of up to 75.000 images from the synthetic datasets (Figure 6),
while the first layers were trained with the ImageNet dataset. This was performed
by means of transfer learning on the last three fully-connected layers. Shi et al. [52]
used two state-of-the-art CNNs, namely Inception-ResNet-V2 [53] and ResNet-101
[54], in combination with an object detection engine [55] to improve their reliabil-530
ity. Synthetic images generated in the 3DS-Max software were used in combination
with real images to train and test the two networks, specifically 400 and 100 images,
of which 8% were real images, were used for training and testing the networks, re-
spectively. Transfer learning was also performed to adapt the pre-trained networks
to the pose classification of a target spacecraft.535
In a recent effort, Sharma and D’Amico [56] proposed a novel network based on
five convolutional layers and three separate branches (Figure 7). In the first branch,
the Region Proposal Network (RPN) proposed in [55] detects a 2D bounding box
around the target spacecraft. In the other two branches, three fully-connected lay-
ers are used to solve a classification and a regression problem, respectively, and to540
output the relative attitude of the target spacecraft. Then, the bounding box infor-
mation is used together with the attitude information to solve for the relative posi-
tion by minimizing the distance between the corners of the bounding box and the
extremal points of a wireframe 3D model of the target. The training was performed
28
Figure 6: Illustration of the AlexNet architecture adopted in [17].
Figure 7: Illustration of the CNN architecture adopted in [56].
with 12.000 synthetic images of the TANGO spacecraft, whereas two test sets were545
created with 3.000 synthetic images and 300 actual camera images, respectively. Fur-
thermore, half of the synthetic images included the Earth in the background.
The CNN-based algorithm in [17] has been extensively tested against the num-
ber of synthetic images used in the training, different levels of image noise and the
amount of displacement of the target from the center of the image plane, which has550
not been tested in the validation of other pose estimation algorithms. However, sev-
eral improvements are proposed in the paper. First of all, the CNN should be trained
with actual space imagery. This can be clearly seen in Table 6, in which the pose er-
rors considerably increase when the network is tested with real images. Also, larger
29
datasets shall be considered for a comprehensive comparative assessment of the555
CNN architecture with the conventional pose determination architectures. Further-
more, assumptions on the illumination environment, target texture and reflectance
properties shall be investigated to increase the robustness of the pose estimation,
and different CNNs, such as the GoogLeNet, the ResNets and the DenseNet, shall
be traded-off with respect to computational time and accuracy in the pose estima-560
tion, following the promising results reported in [52] for the Inception-ResNet-V2
and ResNet-101. The scheme proposed in [56] proved to return better pose estimates
than the AlexNet scheme while at the same decreasing the size of the training set, as
well as a comparable accuracy in the 2D bounding box detection compared to the
architecture in [56]. Furthermore, it proved to be robust with respect to the Earth in565
the background. However, its performance was found to drop-off at relatively close
distances for which the target is not fully in the camera FoV as well as during poor
illumination conditions close to eclipse, due to inaccurate box detections. Notice
also that, since the training in [17] and [56] has been performed with relative dis-
tances from 3 up to 50 meters as labels, the estimation system for close-proximity570
operations down to docking could not be validated.
Despite the relatively coarse accuracies in the pose estimation, especially in the
relative attitude, neural networks could still improve the pose initialization. As men-
tioned in [17], a feature-based algorithm with a CNN-based pose estimation, which
provides a coarse initial guess, could increase the robustness of the pose initializa-575
tion with respect to scenarios in which the IP fails in extracting the target features
from the image background.
Finally, none of the previous CNN-based pose estimation methods were tested
in a navigation filter, and some effort is still required in the modeling of the mea-
surement noise when neural networks are adopted prior to the filter estimation. It is580
also important to notice that, if the target shape during operation considerably dif-
fers from the one assumed during the training phase, the reliability of CNNs might
be affected. Future works shall assess the impact of such uncertainty in the target
shape on the pose estimation accuracy, as well as investigate the benefits of CNN-
based schemes over feature-based schemes.585
30
Table 6: Comparison of CNN architectures for relative pose estimation. Here, the mean position and
attitude errors, ETand ER, are reported together with the Intersection-Over-Union (IoU) metric, which
measures the accuracy of the 2D bounding box detection.
Ref. Architecture
Training/Test Set
Images
ET[m] ER[deg] IoU
[17] AlexNet
(3.000 pose labels)
75.000/50.000 synthetic
75.000/25 real
0.12
1.12
11.94
30.75
-
[52]
101-layer ResNet
Inception ResNet V2
(with RPN)
400/100 - - 0.88
0.88
[56] Convolutional layers + RPN +
Fully-connected layers
12000/3000 synthetic
12000/300 real
[0.055,0.046,0.78]
[0.036,0.015,0.189]
8.4
18.19
0.8582
0.8596
4. Visual-based Navigation Filters
The relative pose estimation schemes described in Section 3 provide an initial
estimate of the relative position and attitude of a target spacecraft with respect to
the servicer spacecraft for lost-in-space scenarios, in which no a-priori information
of the relative state is available. This is referred to as the pose initialization subsys-590
tem. Once the initial guess on the relative state is computed from the estimation
scheme, pose tracking can be performed by collecting a new camera image and us-
ing the previous state as the new initial state for a subsequent pose initialization
problem. However, the pose initialization routines are not well suited to produce
pose estimates at high frequencies, especially due to the computationally expensive595
IP in combination with the PnP solvers. Therefore, a relative navigation filter shall
be used in combination with the camera measurements and the pose estimation
suite in order to return relative state solutions at high frequency [16]. Furthermore,
the internal dynamics of the filter improve the accuracy of the predicted relative
state from measurements and allow a more robust pose tracking. From a high level600
perspective, two different relative navigation architectures are usually exploited in
31
the framework of the relative pose estimation of an uncooperative target. A tightly-
coupled architecture, where the extracted features are directly processed by the nav-
igation filter without exploiting any model-based method, and a loosely-coupled ar-
chitecture, in which the relative pose is already determined prior to the navigation605
filter, i.e. by adopting a model-based method. When dealing with uncooperative
tumbling targets, a loosely-coupled approach is usually preferred since the fast rel-
ative dynamics could jeopardize the robustness of features tracking, provided that a
simplified geometrical model of the target is available. On the other hand a tightly-
coupled approach is the best option when dealing with unknown targets, since it610
does not rely on any a-priory knowledge of the target geometrical model.
In the framework of spacecraft relative motion, several representations of a lin-
earized relative state exist based on the intersatellite range, orbital eccentricity and
perturbation forces involved. Linearized models are required when the filter inter-
nal dynamics needs to be linearized, as it is the case for linear Kalman FIlter (KF) and615
Extended Kalman Filter (EKF). Ref. [57] provides a detailed overview on closed-form
dynamics model suited for onboard relative navigation. Notice that, for ADR and
On-orbit servicing, the target orbit can usually be assumed to be circular, thus sim-
plifying the computational burden that results from not neglecting the orbital ec-
centricity of satellite orbits. Generally, a distinction is made between models which620
make use of a Cartesian representation of the relative state (position and velocity)
and models which consider a set of the Relative Orbital Elements (ROE). Notably,
perturbation models can be easily accommodated in the filter dynamics in the lat-
ter case [58, 59, 60]. Clearly, a linearized model is not required if nonlinear filters
are adopted. On the other hand, in the context of spacecraft relative attitude, sev-625
eral linear and nonlinear models exist based on either Euler angles, quaternions and
Modified Rodrigues Parameters (MRP) [61, 62, 63].
Navigation systems for close-proximity operations have been extensively vali-
dated in the context of RF and monocular vision navigation for FF and on-orbit ser-630
vicing, when the target is cooperative [64, 1, 65, 61, 66]. However, there is still a
lack of a comprehensive validation of navigation systems for the pose estimation of
32
an uncooperative target. As an example, the EKF and the Unscented Kalman Filter
(UKF) presented in [61] and [66], respectively, rely on the availability of gyro mea-
surements from each spacecraft, which is usually not the case for uncooperative635
spacecraft in ADR scenarios. When the uncooperative target is known, it is assumed
that a simplified geometrical model of the target is available and representative of
the target state in orbit. As such, when a model-based pose estimation method is
adopted prior to the navigation filter, the 3D model of the target can be assumed
to be reliable, and the navigation system can estimate the relative pose based on640
the pseudomeasurements derived from the extracted features of the target without
including uncertainty in the geometrical model. However, if the shape of the target
has changed due to orbit degradation and/or due to unforeseen events, the assump-
tions on its state made in the simplified geometrical model might differ from its real
conditions in orbit. Furthermore, the target’s mass and moment of inertia, together645
with other relevant parameters, might differ from the assumed values. As such, the
navigation filter might have to estimate additional parameters aside from the rela-
tive pose.
4.1. Design and Validation of Monocular Navigation Systems: known targets
When dealing with uncooperative known targets, the state vector to be estimated650
in the navigation filter consists in the relative position, velocity, attitude and angular
velocity between the chaser and the target. Additionally, if the relative dynamics be-
tween the servicer and the target spacecraft, modeled in the relative navigation sys-
tem, account for perturbation models which might be inaccurate, key perturbation
parameters should be included given the uncertainty of the dynamics models. As655
already mentioned, loosely-coupled navigation architectures are usually preferred
when the target is known.
Table 7 lists the state-of-the-art for the navigation filters adopted in the frame-
work of pose estimation of uncooperative known targets. Naasz et al. [12] imple-
mented a Multiplicative Extended Kalman Filter (MEKF) [63] for attitude estimation660
and a linear KF for translation to estimate the pose of the HST, assumed to be un-
cooperative. Furthermore, Sharma and D’Amico [16] proposed a reduced-dynamics
33
Table 7: Comparison of navigation filters for relative pose estimation, together with the adopted perfor-
mance validation method. Here, NS refers to papers in which the adopted filters were not specified
Ref. Translational filter Rotational filter Performance Validation Method
[12] Linear KF MEKF Ground-based test on HST mockup
[16] MEKF MEKF Numerical simulations
[67] Linear KF Linear KF HIL in closed GNC loop
[22] MEKF/
Schmidt KF
MEKF/
Schmidt KF Numerical simulations
[68] D-Q MEKF D-Q MEKF Ground-based experimental test
[69] NS NS SIL/HIL in closed GNC loop
[70] DA filters DA filters Numerical simulations
[71] -
Minimum Energy Filter
Attitude Observer
2nd Order Minimum Energy Filter
MEKF
Numerical simulations
[19] Hfilter 2nd Order Minimum Energy Filter Numerical simulations
34
pose estimation in which a MEKF is formulated, validated and stress-tested with the
PRISMA dataset. The measurement model was computed from pseudomeasure-
ments, derived from the line segments detected from the image by the IP, by express-665
ing each line segment as a function of the ROE and of the relative attitude quater-
nion. However, in both implementations the filter dynamics were highly simplified
and no perturbation models were included. Moreover, the initial conditions for the
relative state in [16] were assumed from the separate results of the pose initialization
subsystem, without modeling the interface between the initial pose estimation and670
the filter itself, and no SIL/HIL tests were conducted. Gasbarri et al. [67] performed
a Hardware-In-the-Loop (HIL) experiment in a closed GNC loop using the camera
as a standalone sensor. However, no perturbation models were included in the filter
dynamics and only a simplified linear KF was implemented. Galante et al. [22] pro-
posed the fusion of several measurements from different types of monocular sensors675
and a LIDAR in a MEKF. Their navigation filter was designed assuming that no infor-
mation about the servicer absolute position and velocity is available. As such, they
neglected orbital dynamics in the filter propagation step, and considered a Schmidt
KF [72] to counteract the limited system observability, which results from the lack of
sufficient richness in the relative motion dynamics. Furthermore, the filter state was680
augmented with sensor biases to account for the different optical spectra of the pose
measurement sensors. Filipe et al. [68] validated experimentally a Dual Quaternion
MEKF (DQ-MEKF) [63] suitable for uncooperative satellite proximity operation sce-
narios, in which the pose measurements are rearranged in a dual quaternion form
and fed into the navigation filter. Their filter proved to be fast enough for operational685
use and insensitive to singularity problems, due to its error formulation. However,
only limited scenarios were simulated in the tests. Colmenarejo et al. [69] performed
a comprehensive ground testing to investigate system, as well as subsystems, level
considerations related to several ADR scenarios. A complete GNC model designed in
a FES was Software-In-the-Loop (SIL)/HIL-tested, thus accounting for the interfaces690
between the navigation filter, the IP and the initial pose estimator. Results validated
several aspects of the filter robustness, such as information about the illumination
quality and sensitivity to blackouts. However, several challenges behind fusing dif-
35
ferent absolute and relative sensors in the navigation filter were not solved, and the
robustness of the navigation filter was not fully investigated. Furthermore, the test-695
ing did not account for recent IP methods, and the robustness of the filter with re-
spect to a tumbling scenario was not assessed. Cavenago et al. [70] proposed two
innovative nonlinear filters based on Differential Algebra (DA) to limit the compu-
tational time while preserving the filter performance. Their design included relative
rotational dynamics which account for the apparent torques, the servicer-inertial700
torques and the target inertia matrix, thus improving other models which assumed
simplified, unperturbed relative rotational motion. However, only a simplified soft-
ware was used for the validation of the navigation system. In a recent effort, Pesce
et al. [71] decoupled the translational and rotational motion, and compared nonlin-
ear filtering techniques to a MEKF for the relative attitude estimation of an unco-705
operative target. Nonlinear filtering algorithms such as the Minimum Energy Filter,
the Attitude Observer [73, 74], and the 2nd Order Minimum Energy Filter [75] were
adapted for the specific application. Compared to the analysis conducted in [70],
the filters performance was assessed by considering limited knowledge on the tar-
get inertia matrix by neglecting the relative dynamics in their formulation. Their710
results showed that, despite a quicker convergence in transient, the MEKF has a
lower performance at steady-state when compared to the nonlinear filters. Further-
more, the second-order minimum energy filter without dynamics was proposed as
the best option in scenarios where neither the angular velocity nor the inertia matrix
of the target are fully known. Furthermore, Pesce et al. [19] proposed a novel nav-715
igation system in which a HFilter [76] was selected for the translational motion
estimation and the 2nd Order Minimum Energy Filter for the rotation motion esti-
mation, respectively. The translational filter implemented the Yamanaka-Ankersen
[77] formulation of satellite relative motion, and it was chosen based on the claim
that assumptions of KF are usually not satisfied when dealing with optical systems,720
and on the fact that the absolute position of the servicer, together with the illumi-
nation conditions, can strongly affect the process and measurement noise if a KF
is selected. Their design returned a navigation system for which filter robustness is
preferred rather than filter optimality. On the other hand, the selected rotation filter
36
was characterized by a null derivative of the angular acceleration, in order to avoid725
the dependence of the filter accuracy on the knowledge of the inertia matrix of the
target spacecraft. Despite the worse performance compared to filters that include
the relative dynamics, and thus the inertia matrix of the target, the proposed for-
mulation could be extended for the pose estimation of partially known targets. Re-
sults obtained by considering Low Earth Orbit (LEO), Highly Elliptical Orbit (HEO)730
and GEO scenarios showed a steady state relative position and attitude Root-Mean-
Square Error (RMSE) lower than 3 cm (except for HEO) and 1 degree, respectively.
Notice also that no perturbation models were included in both filters.
An important aspect of the relative navigation filter reviewed so far relates to
whether the absolute state of the servicer spacecraft is required to estimate the rel-735
ative state between the servicer and the target. Except for the design in [22], the re-
viewed filter designs assumed that the absolute state of the servicer is known, which
implies that absolute sensors such as GPS and/or Inertia Measurement Units (IMU)
shall be included in the absolute filter. However, GPS can increase complexity to
the system and it is not being considered in some of the current designs for close-740
proximity rendezvous missions. On the other hand, the limited accuracy of the ab-
solute position and velocity information from an IMU onboard the servicer would
probably result in a decreased accuracy in the estimated relative state, when com-
pared to the estimation accuracy results obtained by assuming no noise in the ab-
solute position and velocity. It can be stated that the interface between the relative745
and absolute navigation filters onboard the servicer spacecraft still presents open
issues. Future research should investigate more filter designs which do not rely on
the servicer absolute position (and velocity) by solving the challenges of a simplified
orbital dynamics model. At the same time, the impact of the measurement noise
of the servicer position on the relative navigation filter should be assessed for those750
designs which include the servicer absolute position (and velocity).
37
4.2. Design and Validation of Monocular Navigation Systems: partially known tar-
gets
During close-range rendezvous, the relative attitude dynamics is strongly de-
pendent on the target’s moment of inertia, which might be partially unknown for755
inactive satellites. At the same time, the knowledge of the location of the center of
mass is critical for a safe approach to the target. As such, it is important to include
the estimation of these parameters in the navigation filter, in order to improve the
knowledge of the target state as well as of its orbit relative to the servicer.
The position and velocity of the center of mass can be estimated by solving a760
least squares problem in which the position and velocity of the geometrical center,
or of a feature point, on the target body are measured by a monocular camera [78,
79]. Alternatively, Al-Isawi and Sasiadek [80] calculated the location of the center of
mass using kinematic equations and an Iterative Closest Point (ICP) algorithm, and
Meng et al. [81] implemented an EKF and additionally estimated the target body765
mass by applying an impulse to the target.
Several approaches exist in literature to estimate the target moment of inertia
with stereo cameras or other active sensors such as LIDARs. The interested readers
are referred to the survey in [10] for a comprehensive overview. However, there are
more restrictions on system observability when monocular cameras are adopted.770
Sheinfeld and Rock [79] presented a framework for rigid body inertia estimation
for torque-free and non torque-free motion applicable to monocular vision. Fol-
lowing these findings, Benninghoff and Boge [78] and Qiu et al. [82] proposed two
methods based on kinematic equations and the conservation of angular momen-
tum, in combination with a constrained least squares method, to ensure positive775
diagonal values of the inertia matrix. Additionally, Hou et al. [83] proposed a dual
vector quaternions-based EKF and a dual vector quaternions-based adaptive fading
factors EKF to estimate the ratios of the inertia parameters of a free-floating tum-
bling space target. In all these methods, only normalized moments of inertia were
estimated, since no external torques were applied on the target spacecraft. Setter-780
field et al. [84] proposed a method to additionally estimate the three principal axes
together with the inertia ratios through the analysis of the target object’s polhode in
38
an arbitrary target-fixed geometric frame. Felicetti et al. [85] analyzed the estimation
of the full inertia matrix by exerting a control torque on the object and by adopting
an EKF. However, their method is applicable only to estimate the moment inertia785
of the multibody system once the chasing and the grasping phases have occurred.
Xu and Wang [86] investigated the possibility to estimate the target inertia by using
the information of the mass and velocity of a bullet shot to the target to change its
angular momentum. Recently, Meng et al. [81] proposed a different method based
on the application of a number of impulses to the target in order to observe the re-790
sulting motion changes and solve for all the inertia parameters. An EKF was used
to estimate the normalized inertia matrix together with the target mass, and a least
squares method was added to estimate the full set of inertial parameters.
5. Conclusions and Recommendations
This paper presented a detailed review of the robustness and applicability of795
state-of-the-art monocular pose estimation systems for the relative navigation with
an uncooperative spacecraft. The research is motivated by the applicability of rela-
tive pose estimation in future space missions, i.e. ADR and IOS, which involve close-
proximity operations of a servicer spacecraft around a target. Monocular systems
were reviewed due to the strict power, mass, and operational range requirements800
driving the current design of these missions, which are usually killer requirements
for active, as well as stereo, systems.
First, a review of monocular EO systems is given in which VIS, TIR and NIR cam-
era suites are traded-off against image quality and robustness with respect to the
space environment. Due to the limited robustness of VIS/NIR cameras against harsh805
illumination conditions and the presence of the Sun or the Earth in the background,
and the limited image quality which characterizes TIR cameras, multispectral sys-
tems are identified as a promising solution capable of increasing the overall system
robustness, while at the same time preserving system accuracy. Furthermore, the
applicability of the state-of-the-art camera suites to operational ranges from several810
hundreds of meters down to docking is analyzed in order to assess whether only a
39
single monocular camera could be used during close-proximity operations. In prin-
ciple, collaborative cameras could be avoided by switching to feature tracking as
soon as the target is not fully in the camera FoV.
Monocular pose estimation is analyzed by firstly focusing on the IP algorithms815
adopted prior to the actual estimation. Three main feature synthesis schemes are
identified which are able to combine the advantages of several feature detectors into
a more robust system. Furthermore, it is foreseen that the combination of keypoint
detectors with edge and corner detectors will represent an additional step forward
in the design of robust and reliable IP systems, provided that keypoint detectors are820
validated against scenarios in which the Earth is in the image background. Besides,
it is expected that feature detection methods based on neural networks will improve
the system robustness against adverse illumination conditions as well as partial oc-
cultation of the target.
The different techniques adopted for the pose initialization and tracking are then825
reviewed. A comparative assessment of several PnP solver is presented, from which
it is concluded that a combination of different solvers should, in principle, improve
the pose estimation accuracy. Furthermore, the challenges involved in VIS-based
and TIR-based estimation systems are listed in terms of the image database adopted
for the validation, the robustness against image background and spacecraft symme-830
try, and the associated IP system adopted. The comparison suggests to investigate a
pose estimation system in which model-based and non-model based methods are
used to estimate the pose from VIS and TIR images, respectively. This follows from
the challenges in relying on an off-line TIR model of the target spacecraft, due to fast
variations in the space thermal environment.835
A review of recent pose estimation systems based on CNNs is provided in or-
der to investigate the level of accuracy that could be achieved by exploiting them
during pose initialization. Three novel methods are reviewed which adopt transfer
learning of pre-trained networks to solve for the relative pose. In particular, the com-
parative assessment showed that a relatively small training database could be used840
without affecting the network performance, provided that suitable network layers
are selected. Additionally, it is suggested that the coarse accuracy, which charac-
40
terises the networks reviewed in this paper, could be compensated by including a
PnP solver which uses the CNN solution as initial guess for the relative pose. Still,
the drop in performance when the target spacecraft is not fully in the camera FoV,845
together with the amount of realistic images to use during training and/or testing,
represent unanswered questions which will require further analyses.
Finally, visual-based navigation filters are reviewed by assessing their applicabil-
ity to scenarios in which the target spacecraft is fully or partially known. The com-
parison between different filters shows that filter selection for the pose estimation850
of an uncooperative target is, from a high-level perspective, driven by a trade-off be-
tween filter robustness and filter optimality. In particular, when the target is partially
known, the dependence of the filter on the target inertia matrix could be tackled by
simplifying the filter internal dynamics, or by estimating the target mass and inertia
in-flight. Furthermore, the qualitative comparison suggests that the impact of the855
absolute filter’s solution on the relative pose estimation should be accounted for in
the design of the navigation filter.
Aknowledgements
This study has been funded and supported by the European Space Agency and
Airbus Defence and Space under Network Partnering Initiative (NPI) program with860
grant number NPI 577 - 2017.
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... Among the vision based methods using these cameras, monocular pose estimation techniques [9,8,7] are drawing a special attention for their wide range of applications particularly in on orbiting servicing mission i.e OOS, shown in Figure 1. Section 2 includes a more detailed discussion on this type of application. ...
... vector with Linear(128, 9) layer. Relu unit are kept in between to add more non-linearity while also adding a dropout of 0.5 each time to avoid over-fitting. ...
Thesis
6D Satellite pose estimation is a crucial step of many on orbit servicing missions. Either for docking or debris removal, one need to know the pose of the target satellite. Throughout the time many methods were proposed to solve this problem, till the recent surge of monochrome based deep learning methods. We particularly get interested in data demanding direct regression methods using neural networks. We generate photorealistic data using Blenderproc and we divide many experiments to probe some architectural designs improving the networks performance, such as the choice of representations, hyperparameters and loss functions. Using all the feedback from previous experiments we go on optimizing the results.
... Using observations collected by a number of chaser spacecraft orbiting in formation can improve accuracy and robustness of the relative positioning estimation [5]. It is an actively developing field, with recent applications from machine learning techniques, particularly deep neural networks (DNNs) to increase the accuracy and reliability in variable and challenging lighting conditions [74,75]. Neural networks can be used in a tightly-coupled or in a looselycoupled approach [74]. ...
... It is an actively developing field, with recent applications from machine learning techniques, particularly deep neural networks (DNNs) to increase the accuracy and reliability in variable and challenging lighting conditions [74,75]. Neural networks can be used in a tightly-coupled or in a looselycoupled approach [74]. In the first case, a neural network is trained for feature classification [76,77], in which the output of the network is a set of features matches to be fed directly to the navigation filter or processed by a pose solver that estimates relative position and attitude parameters. ...
Article
Relative navigation of multiple spacecraft based on the processing of Global Navigation Satellite System (GNSS) signals has been successfully performed in Low Earth Orbit (LEO). An accuracy on the order of one millimeter for relative positioning can be achieved in post-processing by using Carrier Phase Differential GNSS (CDGNSS) and recovering the correct carrier phase integer ambiguity. However, the real-time performance and accuracy without post100761-processing can be much lower because of limited on-board computational resources, signal obstruction, multipath interference induced by other spacecraft orbiting in proximity, and inaccurate knowledge of the spacecraft’s attitude. The navigation accuracy can be considerably degraded in higher Earth orbits above the GNSS constellation because of weaker signals and poor Geometric Dilution of Precision (GDOP). On the other hand, spacecraft navigation based on monocular vision is typically not as accurate as the one based on CDGNSS. In this paper, we review a novel multi-sensor navigation approach that utilizes a tight fusion of carrier phase GNSS observations and monocular measurements which enables fast and robust autonomous relative pose estimation of cooperative spacecraft in a degraded GNSS environment. We consider significant tracking noise and high operation orbits above the GNSS constellation characterized by weaker signals, lower visibility, higher GDOP, and low GNSS visibility. The architecture and algorithms are evaluated using both simulated Geosynchronous Earth Orbit data and real GPS data collected in an urban environment, thereby demonstrating that fusing GNSS with vision can provide better accuracy, robustness, and faster GNSS integer ambiguity resolution than when using GNSS only, especially in degraded GNSS conditions.
... The vision-based navigation is a classical approach for non--cooperative target attitude estimation due to the advantages of light weight and low energy consumption [3]. Camera-based attitude estimation algorithms generally require the pre-knowledge of the features of the target, and determine the relative attitude by matching the features among different images. ...
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Full-text available
In this paper, the relative navigation problem of a large–scale free tumbling non–cooperative target is investigated. Given that the target cannot be completely captured by navigation sensors in an ultra–short range due to its large size, a hybrid relative navigation algorithm is proposed based on the data fusing principle of monocular cameras and Lidar sensors. First, an algorithm of vision aided point cloud segmentation was developed to improve the data processing efficiency of Lidar. Next, an attitude determination method was proposed for the target via a pure geometrical approach, which is robust to sensor errors. Then, a target geometrical recognition was performed based on a simultaneous localization and mapping (SLAM) approach. Finally, the feasibility and effectiveness of the proposed methods were verified by numerical simulations, indicating that the new relative navigation framework is capable of more accurate attitude tracking of the target with synchronous geometrical recognition than classic attitude tracking methods.
... Kothari et al. [4] collated various applications of DL for space, briefly discussing the achieved and prospective goals of onboard systems for spacecraft positioning during docking and landing. Aiming at non-cooperative spacecraft rendezvous specifically, Cassinis et al. [5] first provided a review of CNN-based schemes in the context of monocular pose estimation systems discussing in detail several works (e.g. [6][7][8]). ...
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... As such, compact and lightweight passive digital cameras have become the cost-effective sensor for the task. Accordingly, the last couple of decades have focused on the development of robust image processing (IP) and machine learning (ML) techniques to accurately estimate the target's six degree-of-freedom (DOF) pose from images obtained aboard the chaser [3]. As the target is generally known beforehand, the followed strategies often choose to solve the model-to-image registration problem, under which the pose is retrieved via perspective-n-point (PnP [4]) and RANSAC-based (random sample consensus [5]) methods from correspondences between two-dimensional image features and three-dimensional model points. ...
Preprint
This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with red-green-blue (RGB) inputs, thus mitigating the effects of artefacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.
... Kothari et al. [3] collated various applications of DL for space, briefly discussing the achieved and prospective goals of onboard systems for spacecraft positioning during docking and landing. Aiming at non-cooperative spacecraft rendezvous specifically, Cassinis et al. [4] first provided a review of CNN-based (Convolutional Neural Network) schemes in the context of monocular pose estimation systems discussing in detail several works (e.g. [5,6,7]). ...
Preprint
Autonomous spacecraft relative navigation technology has been planned for and applied to many famous space missions. The development of on-board electronics systems has enabled the use of vision-based and LiDAR-based methods to achieve better performances. Meanwhile, deep learning has reached great success in different areas, especially in computer vision, which has also attracted the attention of space researchers. However, spacecraft navigation differs from ground tasks due to high reliability requirements but lack of large datasets. This survey aims to systematically investigate the current deep learning-based autonomous spacecraft relative navigation methods, focusing on concrete orbital applications such as spacecraft rendezvous and landing on small bodies or the Moon. The fundamental characteristics, primary motivations, and contributions of deep learning-based relative navigation algorithms are first summarised from three perspectives of spacecraft rendezvous, asteroid exploration, and terrain navigation. Furthermore, popular visual tracking benchmarks and their respective properties are compared and summarised. Finally, potential applications are discussed, along with expected impediments.
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This work addresses the design and validation of a robust monocular vision-based pose initialization architecture for close-range onorbit-servicing and formation-flying applications. The aim is to rapidly determine the pose of a passive space resident object using its known three-dimensional wireframe model and a single low-resolution two-dimensional image collected on board the servicer spacecraft. In contrast to previous works, the proposed architecture is onboard executable and capable of estimating the pose of the client without the use of fiducial markers and without any a priori range measurements or state information. A novel feature detection method based on the filtering of the weak image gradients is used to identify the true edges of the client in the image, even in presence of the Earth in background. The detected features are synthesized using simple geometric constraints to dramatically reduce the search space of the feature correspondence problem, which is solved using the EPnP method. This approach is proven to be an order of magnitude faster than the state-of-the-art random sample consensus methods. A fast Newton–Raphson method that minimizes the fit error between corresponding image and model features is employed to refine the pose estimate and to resolve pose ambiguity. The proposed methodology is tested using actual space imagery collected during the PRISMA mission at about a 700 km altitude and a 10 m interspacecraft separation.
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