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Multi Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments

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The combination of data from multiple sensors, also known as sensor fusion or data fusion, is a key aspect in the design of autonomous robots. In particular, algorithms able to accommodate sensor fusion techniques enable increased accuracy, and are more resilient against the malfunction of individual sensors. The development of algorithms for autonomous navigation, mapping and localization have seen big advancements over the past two decades. Nonetheless, challenges remain in developing robust solutions for accurate localization in dense urban environments, where the so called last-mile delivery occurs. In these scenarios, local motion estimation is combined with the matching of real-time data with a detailed pre-built map. In this paper, we utilize data gathered with an autonomous delivery robot to compare different sensor fusion techniques and evaluate which are the algorithms providing the highest accuracy depending on the environment. The techniques we analyze and propose in this paper utilize 3D lidar data, inertial data, GNSS data and wheel encoder readings. We show how lidar scan matching combined with other sensor data can be used to increase the accuracy of the robot localization and, in consequence, its navigation. Moreover, we propose a strategy to reduce the impact on navigation performance when a change in the environment renders map data invalid or part of the available map is corrupted.
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Multi Sensor Fusion for Navigation and Mapping in Autonomous
Vehicles: Accurate Localization in Urban Environments
L. Qingqing1,2, J. Pe ˜
na Queralta2, T. Nguyen Gia2, Z. Zou1and T. Westerlund2
1School of Information Science and Technology, Fudan Universtiy, China
2Department of Future Technologies, University of Turku, Finland
Emails: {qingqingli16, zhuo}@fudan.edu.cn, {jopequ, tunggi, tovewe}@utu.fi,
Abstract—The combination of data from multiple sensors, also
known as sensor fusion or data fusion, is a key aspect in the
design of autonomous robots. In particular, algorithms able to
accommodate sensor fusion techniques enable increased accuracy,
and are more resilient against the malfunction of individual
sensors. The development of algorithms for autonomous navi-
gation, mapping and localization have seen big advancements
over the past two decades. Nonetheless, challenges remain in
developing robust solutions for accurate localization in dense
urban environments, where the so called last-mile delivery occurs.
In these scenarios, local motion estimation is combined with
the matching of real-time data with a detailed pre-built map.
In this paper, we utilize data gathered with an autonomous
delivery robot to compare different sensor fusion techniques
and evaluate which are the algorithms providing the highest
accuracy depending on the environment. The techniques we
analyze and propose in this paper utilize 3D lidar data, inertial
data, GNSS data and wheel encoder readings. We show how
lidar scan matching combined with other sensor data can be
used to increase the accuracy of the robot localization and, in
consequence, its navigation. Moreover, we propose a strategy to
reduce the impact on navigation performance when a change in
the environment renders map data invalid or part of the available
map is corrupted.
Index Terms—SLAM; Sensor fusion; Navigation; Localization;
Mapping; Urban navigation; ROS; PCL; 3D Lidar; LOAM; Last-
mile delivery; Autonomous vehicles; Self-driving cars;
I. INTRODUCTION
Accurate mapping and localization is the cornerstone of
self-driving cars. In open roads or highways, lane-following
can partly replace localization while still allowing for au-
tonomous operation [1]. Nonetheless, in dense urban environ-
ments accurate localization is a paramount aspect of a robot’s
autonomous operation [2]. Smaller pathways and more dy-
namic environments pose significant technical challenges [3].
In addition, the robot mission often adds accuracy require-
ments, such as in autonomous post delivery [4]. Multiple
sensors can be utilized to facilitate autonomous navigation and
operation. Among those, visual data provides more semantic
and qualitative information [5], [6], lidar measurements are
more accurate and are able to accurately describe objects from
a geometric point of view [7].
The past decade has seen a boost in the development
of autonomous vehicles for civilian use. Google started the
development of its self-driving technology for cars in 2009 [8],
and since then a myriad of industry leaders [9], [10], start-
ups [11], and academic researchers [12] have joined the race in
Fig. 1. Illustration of the matching process between the pre-acquired map
(red) and current lidar scan (green-blue).
the technology sector, a race to make everything autonomous.
In any mobile robot or vehicle, SLAM algorithms are an
essential and crucial aspect of autonomous navigation [2], [13].
Autonomous robots or self-driving cars will potentially
disrupt the logistics industry worldwide [14]. Autonomous
trucks or autonomous cargo vessels are already in advanced
stages or development and might be seen in operation within
the next five or ten years [15]. However, both technological
and legal challenges remain within the so called ”last-mile”
delivery [16]. Last-mile refers to the last step in the delivery
of goods from a local logistics or supply center to the clients’
door. In this paper, we utilize data gathered using a small
delivery robot from Jingdong, one of the top two e-commerce
platform and B2C online retailer in China.
The development of simultaneous localization and mapping
(SLAM) algorithms has seen a rapid evolution over the past
two decades [17], [18]. In SLAM algorithms, information from
a wide range of sensors can be used to map the environment
and localize the vehicle or robot in real time. These include
inertial measurement units, monocular or binocular cameras,
GNSS sensors, lidars, ultrasonic sensors or radars, or wheel
encoders [19]. Detailed 3D maps in the form of point clouds
can be generated, for instance, from 3D lidars or with stereo
vision [7]. We focus on the study and comparison of different
localization methods for a small delivery robot in dense urban
environments. In these scenarios, an existing map of the
operating environment is obtained in advance and either pre-
loaded or accessible by the autonomous robot. The map is used
in order to obtain more accurate localization by matching each
scan with a certain area of the map in real-time [2], [20].
The local motion of a robot or vehicle can be estimated
directly by integrating data from inertial measurement units,
including accelerometers, gyroscopes and compasses. Alter-
natively, different odometry methods can be applied based
on non-inertial sensors. Visual odometry algorithms utilize
feature extraction and tracking, while lidar-based odometry
uses mostly geometric information [17], [18]. Inertial mea-
surement units can be easily combined with wheel encoders.
Differential GNSS measurements also provide accurate local
motion estimation [21]. Global localization can be estimated
either with GNSS data, or by comparing sensor data with
predefined maps or information gathered a priori. For instance,
different methods exist to match a lidar scan with section of a
3D point cloud that defines a map of the operational area [2],
[20]. Over the past decades, researchers from both industry
and academia have been exploring the utilization of these
methods and their combinations to obtain accurate mapping
and localization. More concretely, scholars often refer to the
combination of different sensor data as sensor fusion or data
fusion. In this paper, we compare different techniques and
provide arguments on the best sensor fusion techniques for a
small delivery robot for last-mile delivery.
The algorithms, analysis and results presented in this paper
were mostly developed during the JD Digital (JDD) Global-
ization Challenge Competition in ”Multi-sensor fusion local-
ization for autonomous driving”. The challenge was a global
competition, with 4 classification divisions depending on the
geographical location of the team. Our team ranked first in the
US division semi-final and classified for the 24h global final in
Beijing, China, where the 4 semi-final winners competed for
the first prize. The available sensor data during the competition
was GNSS and gyroscope data, wheel odometry and the
output from a 16-channel 3D lidar. A map of the area was
given as a 3D point cloud. Multiple datasets exist to test
and benchmark different localization algorithms. However, the
most accurate algorithms are obtained through fine tuning and
parameters specific for the dataset, with different parameters
being potentially necessary to achieve the optimal accuracy in
a different sensor or environment setup [22]. Therefore, in this
paper we have utilized the data provided as part of the JDD
competition as it was gathered from the sensors on-board the
vehicle in order to compare a variety of methods. This ensures
that our algorithms can be implemented on the same robot
without a significant impact to performance.
The main contributions of this work are the following:
(i) to analyze and compare different approaches for vehicle
localization estimation, and the definition of a sensor fusion
approach for accurate localization in urban environments; and
(ii) the introduction of a strategy for rebuilding an area of
a local map when data is corrupted or the environment has
undergone significant modifications.
The remainder of this paper is organized as follows: Section
II introduces related work in localization and odometry tech-
niques based on data available from the on-board sensors. Sec-
tion III describes the dataset used in the paper, delves into the
possibilities of sensor fusion approaches based on the available
information, and provides a strategy to operate in areas where
the available map is either outdated or corrupted. Then, section
IV shows experimental results on the localization accuracy
for four different sensor fusion approaches. Finally, Section V
concludes the work and outlines directions for future work.
II. REL ATED WORK
Autonomous navigatio through 3D mapping with lidars has
been an increasingly popular technology over the last decade,
as lidars can provide high accuracy range measurements when
compared to other sensors. Zhang et al. proposed a method
for lidar odometry and mapping. The authors approached the
odometry problem by extracting lidar point cloud features
from each sweep. Then, the transformation between two
consecutive sweeps can be estimated. In this setup, the lidar
is utilized as an odometry sensor [17]. For 3D mapping,
the most important problem is accurate position estimation,
and 3d lidar data for odometry still can produce accumulated
error after long distance walk. However, the accumulated error
is much lower when compared to camera-based odometry
(especially in unfavourable light conditions) or integration
of inertial data from accelerometers/gyroscopes. State-of-the
art solutions combine lidar and monochrome camera sensors
as visual-lidar odometry to improve the performance of ego-
motion estimation [18].
In SLAM algorithms, localization and mapping are done
concurrently in real time. However, in order to achieve lo-
calization with centimeter accuracy in an urban environment,
map matching techniques have emerged over the past few
years. Currently, one of the most widely used approaches for
3D point cloud matching is Normal Distributions Transform
(NDT) matching [23]. Introduced by Bibel et al., NDT has the
advantages of no requiring explicit assignments of relation-
ships between features or subsets of points, and the analytic
formulation of the algorithms. The former aspect increases
the robustness of the algorithm, while the latter reduces the
computational cost and accuracy of the implementation.
Multiple improvements of the NDT algorithm have been
proposed. Gonzalez Prieto et al. presented DENDT, an al-
gorithm for 3D-NDT scan matching with Differential Evo-
lution [20]. The authors utilize a differential evolution (DE)
algorithm in order to improve the optimization process for
finding the solution of the NDT method. Akai et al. presented
a robust localization method that uses intensity readings from
the lidar data in order to detect road markings and use them
for matching consecutive scans [2]. While the method is able
to provide very high accuracy in some environments, the ex-
istence of road markers significantly impacts the performance
and therefore this method cannot be used in all situations.
Wen et al. recently analyzed the performance of different
NDT-based SLAM algorithms in multiple scenarios in Hong
Kong [24]. A valuable conclusion from this work is that the
best performance was achieved in areas with more sparse point
clouds and nominal traffic conditions, while the performance
Fig. 2. On the left, a map with noise added in some areas to simulate corrupted data. On the right, one of
the two corrupted sections of the the map has been restored using GNSS, IMU and lidar odometry.
decreased in dense urban areas. In this work, we study how can
we combine different sensor information in order to improve
localization performance in an urban area.
III. LOCALIZATION AND MAP PIN G
In this section, we describe the dataset that we have utilized
and the different localization approaches. We also introduce a
strategy for situations where the existing map data is corrupted,
outdated, or part of the data is missing.
A. Dataset
The data utilized in this paper was provided as part of
the JD Discovery Global Digitalization Challenge from De-
cember 2018 to January 2019. The data was gathered using
JD’s autonomous last-mile delivery robot, depicted in fig. ??
on page ??. The data includes: (1) GNSS directional and
positional data referenced in the World Geodetic System
(WGS84) format; (2) lidar data as a 3D point cloud; (3) raw
accelerometer and gyroscope data; and (4) wheel speed meter.
Ground truth data is provided as well. The output of the 3D
lidar is given at 10 Hz, IMU data is acquired at 100 Hz and
GNSS data is updated at 5 Hz. In addition, a map of the
objective operation area is given. The map represented as a
point cloud is shown in red in fig. 1 on page 1. The dataset
contains sensor data recorded in an 800-second long closed
loop movement.
In order to both read and process data, ROS has been
utilized. ROS (Robot Operating system) is an open source op-
erating system for robots, which provides a publish-subscribe
communication framework that allows for rapid development
of distributed robotic systems [25]. ROS provides algorithm
reuse support for robot research and development, as well as
abstraction of data models for easier integration of different
modules. PCL (Point Cloud Library) is a cross-platform open
source C++ library, which implements common algorithms and
data structures of point clouds [26]. It can realize point cloud
acquisition, filtering, segmentation, registration, retrieval, fea-
ture extraction, recognition, tracking, surface reconstruction,
visualization and so on. If OpenCV is the crystallization of
2D visual data acquisition and processing, PCL has the same
position in the 3D geometrical data domain.
B. Localization methods
Based on the available sensor data, we have utilized five
different approaches to estimate the vehicle’s localization. In
each approach, we use a different combination of sensors and
describe how the robot position is calculated based on their
data.
GNSS-based localization
One of the most traditional methods for outdoor robot
localization is to use a global navigation satellite system.
In this case, data from multiple satellite constellations was
available and used for increase accuracy. GNSS data error
are mostly caused by the atmospheric conditions and multi-
path interference. The effect of the environment in a larger
scale and the atmospheric conditions can be minimized using
differential GNSS readings, and assuming that the real-time
error is equivalent to the error obtained in a near known
location with which the system is synchronized. However, in
this work we have not relied on differential GNSS.
GNSS+IMU localization
We can easily combine GNSS data with inertial data,
including both accelerometer and accuracy. As differential
GNSS has not been implemented in this case, instead, the
results labelled as ”IMU” utilize the IMU readings for local
motion estimation, and the GNSS reading for an initial global
estimation and estimations when the robot movement is almost
zero for a prolonged period of time.
Lidar odometry (LOAM)
Zhang et al. introduced lidar odometry as an alternative to
the more classical visual odometry techniques [17]. As with
many odometry approaches, features are extracted from data
and compared within consecutive frames, or scans in the case
of a lidar. Features extracted from lidar data are usually based
on geometrical aspects. These include corners and surfaces,
for instance. Because lidars are able to provide high accu-
racy distance measurements even for objects far away from
the sensors, lidar-based odometry is able to provide higher
accuracy than visual-based odometry in open space situations
with clearly differentiated objects. An implementation based
on Zhang’s algorithm has been used in this case.
NDT-based localization (NDT+)
The NDT algorithm is a kind of registration algorithm
that uses the existing high-precision point cloud map and
real-time 3D lidar point cloud data to achieve high-precision
localization.
NDT algorithm does not directly compare the distance
between points in point clouds map and points in lidar point
clouds. First, the NDT algorithm will transform the point cloud
map into the normal distribution in three-dimensional.
If a variable Xis normal distribution X(µ, δ), then it
can be described as:
f(x) = 1
δ2πe
(xµ)2
2δ2(1)
where µis the mean of the variable distribution and δ2is the
variance. For a multivariate normal distribution, its probability
density function can be expressed as:
f(~x) = 1
(2π)D
2p|P|e(~x~µ)TP1(~x~µ)(2)
Where ~x represents the mean vector and Prepresents the
covariance matrix.
The first step of the NDT algorithm is to divide the point
cloud into a 3D grid coordinate. For each cell, the probability
distribution function(PDF) is calculated based on the points
distribution density in the grid.
~µ =1
m
m
X
k=1
~yk(3)
Σ=1
mX
k=1
m(~yk~µ)( ~yk~µ)T(4)
Where ~yk= 1,2,3, m denotes all lidar points in a grid.
Then the PDF can be expressed as:
f(~x) = 1
(2π)3
2p|Σ|e(~x~µ)TP1(~x~µ)(5)
We use the normal distribution to represent the discrete
points of each grid. Each probability density function can be
considered as an approximation of a local surface. It not only
describes the location of the surface in space but also contains
information about the direction and smoothness of the surface.
After calculated the PDF of each grid, then our goal is
to find the best transformation. The lidar point cloud set is
X=~x1, ~x2, ..., ~xn, and the parameter of transformation is ~p.
The spatial transformation function T(~p, ~xk)represents using
transformation ~p to move point ~xk, combined with the previous
calculated state density function(the PDF of each grid), so
the best transformation ~p should be the transformation of
maximum likelihood function:
Likelihood :θ=
n
Y
k=1
f(T(~p, ~xk))
And the maximum likelihood is equivalent to minimum neg-
ative logarithmic likelihood:
log θ=
n
X
k=1
log f(T(~p, ~xk))
The task now becomes to minimize the negative logarithmic
likelihood by using an optimization algorithm to adjust the
transformation parameter ~p. we can use the Newton method
to optimize the parameters.
The main problem of the NDT approach is its stability
when used standalone. As indicated by the authors of previous
works, NDT alone has the disadvantage of being unstable
depending on the scenario [24]. Therefore, we utilize GNSS
data for setting the initial position as well as resetting the
NDT localization method when a sudden change in position
or orientation estimation is detected. In the results, we refer
to this method as NDT+, and a close implementation to the
one provided in existing NDT method has been used [24].
NDT+IMU localization (NDT++)
The final localization utilized in our experiments consisted
on integrating the IMU data into the NDT+ method described
above that uses lidar and GNSS data. With this approach,
we have been able to eradicate the instabilities of the NDT+
method and increase its accuracy.
The algorithm workflow is as follows: first, on system start-
up or reset, GNSS data is used in order to obtain an initial
estimation of the robot’s location. This estimation can be
utilized in order to reduce the area of the map in which the
NDT matching will be looked for. Second, when the robot
starts moving, an unscented Kalman filter that uses IMU data
as input serves as an estimation between lidar scans. The
Kalman filter output is then feeded to the NDT algorithm for
scan matching with the predefined map. The GNSS data is still
used to avoid instabilities, even though we have not detected
any in the dataset utilized in this paper.
The NDT+ and NDT++ approaches have an additional
benefit over the lidar odometry method. In autonomous robots
moving in an urban environment, it is essential to react on time
to obstacles and to have localization information as frequently
as possible. A lidar-only approach has the disadvantage of
receiving sensor updates at only 10Hz in this case. With IMU
readings having a refresh rate of 100Hz, the IMU can be
utilized to obtain local movement estimation between lidar
scan matches using the NDT approach. This minimizes the
possibilities of instabilities in the NDT algorithm as the match-
ing possibilities are reduced and the goal of the algorithm
partly shifts from coarse localization to increasing the accuracy
of IMU-based movement estimation.
C. Corrupted Map Reconstruction
In an urban scenario, it is impractical to propose a local-
ization method that has a high dependency on the existence
of an accurate map of the operational area without a strategy
for operating in case the map data is corrupted or outdated.
In fig. 2 on the preceding page, we show the map of the
0 200 400 600 800
0
2
4
time (s)
Translational Error (m)
LOAM
NDT++
NDT+
GNSS
Fig. 3. Translational errors of the proposed approaches over time.
0 200 400 600 800
5
0
5
time (s)
Rotational error (°)
LOAM
NDT++
NDT+
GNSS
IMU
Fig. 4. Rotational errors of the proposed approaches over time.
operation area (on the left, in black and white), with two areas
where the data has either been removed completely or noise
has been added to render the NDT algorithm unusable. When
the robot approaches these areas, we are able to detect them by
monitoring the difference between the NDT localization and
GNSS and IMU positioning. When part of the map cannot
be matched with current scans, we utilize lidar odometry and
mapping in order to rebuild the corrupted or missing data.
The result of this process is shown on the right side of fig. 2
on page 3, where one of the corrupted map areas has been
restored in real-time while the robot was travelling through
it using lidar odometry and mapping. Even though it is not
visible in the image, there is a relatively small mismatch in
the map in the area where the robot emerges again into a
mapped environment.
IV. EXP ERIMENT AND RESU LTS
We have applied the five approaches proposed to the given
dataset. The results showing the translational and rotational
localization and orientation error are shown in fig. 3 and fig. 4,
respectively. In these figures, the NDT++ method shows a
stable and very small error though time, both in position and
rotation estimation. The NDT+ without taking into account
inertial data shows a larger error but, more importantly, shows
several instabilities that are corrected from the GNSS data.
In order to be able to compare in more detail the different
methods, and to see whether there exist some background
error or drifting, we show the variability of the localization
error though two sets of boxplots in fig. 5 and fig. 6 on the
next page. The specific values are also listed in Table I. We
TABLE I
LOC ALIZ ATI ON ERR OR MEA N AND STA DARD D EVIATI ON
µrot. σrot µxσxµyσy
GNSS 1.52 0.79 -0.46 0.22 1040.18
IMU -1.24 0.62 n/a n/a N/A N/A
LOAM -0.50 1.88 0.40 0.49 0.10 0.49
NDT+ 0.03 0.87 -0.02 1.51 -0.05 1.13
NDT++ 1030.31 -0.01 0.10 -0.05 0.10
LOAM NDT+ NDT++ GNSS
1
0
1
2
Translational Error (m)
Fig. 5. Boxplots of translational errors for the different approaches: x-error
(red) and y-error (blue).
have omitted the location errors of standalone IMU motion
estimation as the error is significantly higher than the proposed
approaches. However, inertial data is still valuable for local
movement estimation and for orientation estimation.
The translational errors are shown in fig. 5, where the red
boxes show the error in the xcoordinate, and the blue boxes
refer to the ycoordinate error. We have separated the error
because in some cases the error mean differs between them.
That is the case of the GNSS data, which due to atmospheric or
environmental conditions shows a steady negative drift in the x
direction. If consistent through large periods of time, it can be
assumed that it is due to a sensing error in the device itself, or
to environment conditions such a specific multi-path occurring
in the operating area. Therefore, this value can be utilized to
decrease the sensing error in real time during operation. In
order to have a deeper understanding of the distribution of
the GNSS error, we show the histogram of the three errors
(two translational, one rotational) in fig. 7 on the next page.
Only the error in the ydirection has a mean of 0, while
the distribution of the xerror is symmetrical and narrow.
Therefore, it is possible to fix the drift while keeping the same
variance for both components of the translational error. In the
case of the rotational error, it is more complex to correct even
though the distribution is still symmetric.
From fig. 5 and fig. 6 on the next page we can see that the
most stable methods are NDT++ and GNSS, with the NDT+
method providing accurate results for rotation estimation.
However, the NDT+ is highly unstable for position estimation,
with the highest variance of all presented approaches. In
position estimation, all approaches have a relatively small
error after 800 seconds of movement, except for the LOAM
method, which error drifts away from 0 towards the end of
the available data set. Similarly, the IMU constantly drifts in
LOAM NDT+ NDT++ GNSS IMU
4
2
0
2
Rotational Error (°)
Fig. 6. Boxplots of rotational errors for the different approaches in degrees.
1 0 1 2
0
1,000
2,000
3,000
4,000
Error (m, degrees)
Absolute Frequency
GNSS x-error
GNSS y-error
GNSS yaw error
Fig. 7. Distribution of GNSS errors.
terms of orientation estimation but it provides a more stable
measurement than LOAM, HDL+ or GNSS.
In summary, lidar scan matching with a 3D map provides
the highest accuracy for localization, both in terms of position
and orientation. Nonetheless, it is essential to take into account
other sensor data in order to implement a more robust approach
that is less prone to instabilities and depends less on the
operational environment. GNSS and inertial data are essential
for increasing the localization accuracy but also for minimizing
the possibilities of unexpected behaviour in the algorithm.
V. CONCLUSION
Accurate localization in dense urban areas is paramount
in order to solve the autonomous last-mile delivery problem.
Nonetheless, it still presents important challenges. We have
explored the possibilities for localization in a city environment
using 3D lidar data complemented with GNSS and inertial data
using a delivery robot from JD. We have shown the accuracy
of different approaches, assuming that a map of the operation
area is given in the form of a point cloud. In addition, we
have presented a strategy for situations where the map might
be corrupted or the scenario might have undergone significant
changes that rendered the map outdated. We have shown that
3D scan matching is the best approach for localization when
properly complimented with IMU data within an unscented
Kalman filter, and GNSS data.
In future work, we will explore the possibilities of inte-
grating visual data for odometry, as well as using the lidar
odometry together with the inertial data within the Kalman
filter in order to increase the NDT localization accuracy.
ACKNOWLEDGMENT
This work has been supported by NFSC grant No.
61876039, and the Shanghai Platform for Neuromorphic and
AI Chip (NeuHeilium).
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