Towards Collaborative Simultaneous
Localization and Mapping: a Survey of the
Current Research Landscape
Dept. Computer and Software Engineering
Oxford Robotics Institute,
Dept. Engineering Science
University of Oxford
Desay SV Automotive
Dept. Computer and Software Engineering
Motivated by the tremendous progress we witnessed in recent years, this paper
presents a survey of the scientiﬁc literature on the topic of Collaborative Si-
multaneous Localization and Mapping (C-SLAM), also known as multi-robot
SLAM. With ﬂeets of self-driving cars on the horizon and the rise of multi-
robot systems in industrial applications, we believe that Collaborative SLAM
will soon become a cornerstone of future robotic applications. In this survey,
we introduce the basic concepts of C-SLAM and present a thorough literature
review. We also outline the major challenges and limitations of C-SLAM in
terms of robustness, communication, and resource management. We conclude
by exploring the area’s current trends and promising research avenues.
Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-
robot SLAM, has been studied extensively with early techniques dating back as far as the
early 2000s (e.g. (Jennings et al., 1999; Fox et al., 2000; Thrun, 2001; Williams et al.,
2002; Fenwick et al., 2002)). These techniques were introduced only a short time after the
inception of single-robot SLAM by researchers who were already envisioning collaborative
perception of the environment. Although they were small-scale proofs of concept, they laid
the foundations that still shape the ﬁeld nowadays.
arXiv:2108.08325v1 [cs.RO] 18 Aug 2021
Figure 1: Collaborative Simultaneous Localization and Mapping Illustration
After years of conﬁnement to laboratory settings, C-SLAM technologies are ﬁnally com-
ing to fruition into industry applications, ranging from warehouse management to ﬂeets
of self-driving cars. Those long awaited success stories are a strong indicator that C-SLAM
technologies are poised to permeate other ﬁelds such as marine exploration (Paull et al., 2014;
Bonin-Font and Burguera, 2020), cooperative object transportation (Rioux et al., 2015), or
search and rescue operations (Tian et al., 2020c; Lee et al., 2020).
SLAM is the current method of choice to enable autonomous navigation, especially in un-
known and GPS-denied environments. SLAM provides an accurate representation of the
robot surroundings which can in turn enable autonomous control and decision making.
Similarly, in multi-robot systems, C-SLAM enables collaborative behaviors by building a
collective representation of the environment and a shared situational awareness.
Moreover, many ambitious applications remain for multi-robot systems, such as the explo-
ration of other planets (Vitug, 2021; Ebadi et al., 2021). To reach those moonshot goals,
ongoing trends in the research community aim to push the boundaries of multi-robot systems
towards increasingly larger teams, or swarms of robots (Beni, 2004; Kegeleirs et al., 2021),
which potentially allow parallel operations that are more eﬃcient and versatile. However,
this is still largely uncharted territory since current multi-robot applications either involve
very few robots or rely upon large amounts of centralized computation in server clusters.
Current C-SLAM techniques are no exception. They are prone to deteriorated performance
when the team size increases above a few robots, and could be infeasible when minimal or
no prior information is available about the operating environment.
Even though C-SLAM-enabled swarms of robots are still far from reality, C-SLAM remains
a useful tool when operating as few as two autonomous robots. In exploration and mapping
applications, even small teams can yield a signiﬁcant boost in performance compared to a
single robot system (Simmons et al., 2000). Notably, autonomous mapping using C-SLAM
has recently received a lot of attention due to the latest DARPA Subterranean Challenge
(DARPA, 2020) and its potential applications in space technologies (Bezouska and Barnhart,
Thus, this paper presents a survey of the relevant literature on the topic of C-SLAM, aiming
to give a complete overview of the main concepts, current developments, open challenges,
Intra-Robot Loop Closures
Direct Inter-Robot Loop Closures
Indirect Inter-Robot Loop Closures
Extended Kalman Filters
Pose Graph Optimization
Perceptual Aliasing Mitigation
Table 1: Collaborative Simultaneous Localization and Mapping Subﬁelds of Research
and new trends in the ﬁeld. We hope it will help new as well as established researchers to
evaluate the state-of-the-art and oﬀer valuable insights to guide future design choices and
research directions. Compared to previous reviews (Saeedi et al., 2016; Rone and Ben-Tzvi,
2013), this paper provides an update on the tremendous progress in the past ﬁve years. It also
aims for a broader overview of the ﬁeld than surveys covering speciﬁc C-SLAM subproblems
such as map merging (Lee et al., 2012), practical implementations (Kshirsagar et al., 2018),
particle ﬁlter techniques (Gupta and Conrad, 2019), vision-based techniques (Zou et al.,
2019), and search and rescue applications (Queralta et al., 2020).
The rest of this paper consists of seven sections covering the main C-SLAM subﬁelds of
research presented in Table 1: Section 2 presents an overview of the single robot SLAM
problem; Section 3 explains the core diﬀerence with C-SLAM; Section 4 explores the diﬀerent
modules of the C-SLAM front-end and their challenges; Section 5 introduces the C-SLAM
back-end and discusses the diﬀerent inference techniques; Section 6 presents the available
benchmarking datasets; Section 7 discusses the ongoing and future trends in the ﬁelds; and
Section 8 concludes the survey and discusses future research avenues.
2 What is SLAM?
At its core, SLAM is a joint estimation of a robot’s state and a model of its surrounding
environment, with the key assumption that a moving robot performs the data collection
sequentially. On one hand, the robot’s state comprises its pose (position and orientation)
and possibly other quantities such as sensors’ calibration parameters. On the other hand,
the environment model (i.e., the map) consists of representations of landmarks, built with
processed data from the robot’s exteroceptive sensors such as cameras or lidars. This makes
SLAM an essential part of many applications that require building an accurate map of the
surrounding environment, whether it be for collision-free navigation, scene understanding,
or visual inspection by a remote human operator. Since dead-reckoning approaches (e.g.
IMU, wheel or visual odometry) drift over time due to noise accumulation, the environment
map in SLAM is also used internally to correct the robot trajectory when known areas are
re-visited. The recovered links between previously visited locations are called loop closures.
SLAM is useful when neither an a priori map nor localization information are available,
when a map needs to be built, or long-term accurate localization estimates are required.
Common scenarios include robotics applications without external positioning systems, such
as the exploration of unknown indoor environments, caves, mines, or other planets.
2.1 Single-Robot SLAM problem
Formally, the overall goal of SLAM is to maximize the posterior of the map and robot state.
We can formulate this with the state variables Xof both the landmarks (map) and the
robot, and the set of measurements Zacquired by the moving robot (Thrun et al., 2005):
This estimation problem is solved by either updating the current state at each time step
given the new observations (i.e., ﬁltering) or optimizing over the whole trajectory and past
observations (i.e., smoothing).
Although ﬁltering in SLAM is still an active research topic, current state-of-the-art tech-
niques are mostly based on smoothing (Cadena et al., 2016; Rosen et al., 2021). The com-
mon formulation for smoothing techniques is a Maximum A Posteriori (MAP ) estimation
problem that leverages the moving robot assumption by introducing a prior distribution (e.g.
obtained by odometry) over the robot trajectory.
Thus, the SLAM problem for a single robot (α) can be expressed as ﬁnding X∗
α, the solution
of the MAP problem:
p(Xα|Zα) = argmax
The decomposition of the posterior distribution is obtained with Bayes’ theorem: p(Zα|Xα)
is the likelihood of the measurements Zαgiven a certain Xα, and p(Xα) is the prior dis-
tribution of the robot motion state. Intuitively, the SLAM problem ﬁnds the set of state
variables (environment landmarks and robot poses) X∗
αthat is most likely to produce the
measurements Zαgiven a prior estimation p(Xα).
It is important to also note that SLAM is closely related to the well-studied problem of
bundle adjustment in Structure from Motion for which we refer the reader to (¨
Ozye¸sil et al.,
Robot 𝛂 Pose Estimate
Figure 2: Single-robot SLAM Overview
2.2 SLAM Architecture
SLAM systems are commonly divided into a front-end and a back-end, each involving diﬀer-
ent ﬁelds of research. The front-end is in charge of perception-related tasks, such as feature
extraction and data association which are both related to ﬁelds such as computer vision
and signal processing. The back-end produces the ﬁnal state estimates using the front-end’s
outputs. The back-end uses tools from the ﬁelds of optimization, probability theory and
graph theory. In practice, the front-end processes the sensor data to generate ego-motion,
loop closure, and landmark measurements, while the back-end performs the joint estimation
of the map and the robot state. Figure 2 provides an overview of a common SLAM structure
in which the robot trajectory is represented as a graph of poses at consecutive discrete times
(i.e., a pose graph) and the map as a set of observed landmarks (Cadena et al., 2016). In a
3D pose graph, the nodes are the robot poses [R,t]∈SE(3) comprised of a rotation matrix
R∈SO(3) and a translation t∈R3, and the edges represent the relative measurements
between the poses (Barfoot, 2017).
Single-robot SLAM still faces many challenges that consequently apply to C-SLAM such as
its long-term use, its robustness to perception failures and incorrect estimates, or its need
for performance guarantees (Cadena et al., 2016). To circumvent those limitations in their
speciﬁc settings, SLAM and C-SLAM developers often have to adapt the architecture and
consider some trade-oﬀs between the sensors capabilities, the onboard computing power, and
3 What is Collaborative SLAM?
Many tasks can be performed faster and more eﬃciently by using multiple robots instead of
a single one. Whether SLAM is used to provide state estimation to support an application
(e.g. estimate each robot’s position to plan for actions), or whether it is at the core of
the task (e.g. mapping an environment), it is beneﬁcial and sometimes necessary to extend
SLAM solutions into coordinated C-SLAM algorithms rather than performing single-robot
SLAM on each robot.
C-SLAM algorithms aim to combine data collected on each individual robot into globally
consistent estimates of a common map and of each robot’s state. This coordination allows
each robot to beneﬁt from experience of the full team, leading to more accurate localization
and mapping than multiple instances of single-robot SLAM. However, this coordination
introduces many new features and challenges inherent to multi-robot systems.
3.1 Multi-robot systems
In multi-robot systems, data collection and state estimation are no longer entirely located
on a single entity, so there is an inevitable need for communication between the agents (i.e.,
robots, base stations, etc.) which is the crux of the problem.
Moreover, multi-robot systems have additional properties to consider when designing C-SLAM
systems, and taxonomies can be deﬁned to classify approaches and highlight their beneﬁts
and tradeoﬀs. The taxonomy proposed in (Dudek et al., 1993) presents considerations that
are well suited to the C-SLAM problem. It distinguishes approaches according to the fol-
Team size The number of robots in the system. Larger teams usually perform tasks more
eﬃciently but may be harder to coordinate.
Communication range Direct communication between robots is limited by their spatial
distribution and the communication medium. In some cases, robots might be unable
to communicate for long periods of time, while in others they might always be in
range of another robot.
Communication topology The topology of the communication network aﬀects how robots
can communicate with one another. For example, they might be limited to either
broadcast or one-to-one messages.
Communication bandwidth The bandwidth of the communication channel aﬀects what
information robots can aﬀord to share.
System reconﬁgurability The robots will move and are likely to change spatial conﬁgu-
ration over time. This can aﬀect the communication topology and bandwidth.
Team unit processing ability Individual robot’s computational capability can aﬀect the
computation cost of C-SLAM approaches and the distribution of computation tasks.
Team composition Robots can be homogeneous or heterogeneous over several aspects such
as locomotion methods and available sensors.
The main diﬀerences between most C-SLAM techniques in the literature lie in the properties
of the multi-robot system considered, especially their resource management strategy. One
subclass of multi-robot systems particularly relevant to the future of C-SLAM are swarm
robotics systems (Brambilla et al., 2013), which are inspired by social animals. Two main
characteristics are required for swarm-compatibility in C-SLAM: robots’ sensing and com-
munication capabilities must be local, and robots can not have access to centralized control
and/or to global knowledge. Such systems would present considerable beneﬁts: they would
have robustness to the loss of individual units, and they could scale well to large numbers of
3.2 C-SLAM Problem deﬁnition
When all robots’ initial states are known or can be estimated, the C-SLAM problem is a
simple extension of the single-robot SLAM MAP problem that includes all the robots’ states,
measurements, and additional inter-robot measurements linking diﬀerent robots’ maps. In
a setup with two robots (α,β), where Xαand Xβare the state variables from robot α
and βto be estimated, Zαand Zβare the set of measurements gathered by robot αand β
independently, Zαβ is the set of inter-robot measurements linking both robot maps containing
relative pose estimates between one pose of robot αand one of robot βin their respective
trajectories, and X∗
βare the solutions, the problem can be formulated as:
p(Xα, Xβ|Zα, Zβ, Zαβ )
p(Zα, Zβ, Zαβ |Xα, Xβ)p(Xα, Xβ)(3)
However, when the relative starting locations and orientations of the robots cannot be de-
termined, the initial guess of the robots states p(Xα, Xβ) is not available. In that case, there
are inﬁnite possible initial alignments between the multiple robot trajectories. Therefore,
in absence of a prior distribution, C-SLAM is reduced to the following Maximum Likelihood
Estimation (MLE) problem.
p(Zα, Zβ, Zαβ |Xα, Xβ) (4)
The C-SLAM problem formulation is still evolving to this day and progress still needs to be
made to achieve an eﬃcient decentralized, distributed and robust implementation. To give
some perspective, Figure 3 presents some major milestones in the evolution of the C-SLAM
problem over time. More details on these milestone works are provided in the following
3.3 C-SLAM systems key properties
3.3.1 Centralized, Decentralized and Distributed
Diﬀerent C-SLAM solutions might be preferred depending on the properties of a given multi-
robot system. One useful and common distinction focuses on how the computation tasks
Fox et al., 2000
(Howard, 2006; Carlone et al., 2011)
Figure 3: C-SLAM Problem Major Milestones
are shared within the team. To that end, most C-SLAM techniques are characterized using
the terms centralized, decentralized and distributed (Leung, 2012). By this classiﬁcation, in
a centralized system, some core computation tasks are done by one speciﬁc robot or a base
station. Conversely, in a decentralized system, the computation can be performed by any one
or more of the robots in the team. Aside from the centralized/decentralized classiﬁcation, a
system is distributed if the computation load is divided among the robots. The two notions
are independent. Therefore, a system could be centralized and distributed at the same time,
if, for example, each robot performs parts of the computation, but a central node is required
to merge the individual results from all the robots.
The preferred choice of centralization and distribution of a C-SLAM solution is strongly
connected to the multi-robot system’s team size, communication topology, communication
bandwidth and team unit processing ability. A swarm-compatible C-SLAM solution would
need to be both decentralized and distributed.
3.3.2 Global versus Local
Another important distinction in C-SLAM, and in multi-robot systems in general, is the
diﬀerence between the global and local perspectives. The local perspective is the default
point of view in single-robot SLAM. Accordingly, the pose and map estimates are expressed
in an internal reference frame which is usually the starting location of the robot’s mission.
However, in C-SLAM, one has to consider the global perspective of the system since the pose
and map of each robot need to be expressed in a shared global reference frame. This means
that every landmark can be expressed within the same coordinates system by every robot in
the team. Otherwise, shared information (e.g. position of observed landmarks) would have
no signiﬁcance to the receiving robot due to the representation being in another unknown
local reference frame. Establishing this global reference frame using C-SLAM allows the
robots to collectively perceive the environment and beneﬁt from each other’s observations.
To achieve this global understanding, one could either solve C-SLAM directly from the
global perspective or solve it from a local perspective that is later aligned to the global
reference frame. In the ﬁrst option, the estimator has an omniscient view of the entire team
of robots: it performs the estimation given perfect knowledge of the measurements of each
robot. These measurements can be raw or preprocessed, and shared on demand depending
on the communication limits. This approach is best suited for centralized systems.
Unfortunately, solving C-SLAM from the global perspective quickly becomes intractable as
the number of robots increases (Saeedi et al., 2016). Thus, a better solution for scalability is
to solve C-SLAM from the local perspective (Cieslewski et al., 2018). This means that each
robot only has access to its own data and partial information from its neighbors. Therefore,
it cannot solve the C-SLAM problem for all the robots at once, but it aims for a local
solution for each robot that is consistent with its neighbors. Then, iteratively and over time,
with the robots gradually improving their estimates given the neighbors’ latest data, local
techniques converge to local solutions that are mutually consistent across the team of robots.
So, upon convergence, the individual robots reach a common understanding and their local
maps are aligned with the common (global) reference frame. Figure 4 provides examples of
the C-SLAM problem and output in both perspectives.
Robot 𝛂 Pose
Robot 𝛃 Pose
Robot 𝛄 Pose
map and trajectories estimate
(a) Global Perspective in C-SLAM
Globally consistent local
map and trajectory estimate
Robot 𝛂 Pose
Partial Robot 𝛃 Info
Partial Robot 𝛄 Info
(b) Local Perspective in C-SLAM
Figure 4: Illustration of the global and local perspectives approaches to solve the C-SLAM
C-SLAM from the Global Perspective Many seminal C-SLAM works are centralized
and solve the estimation problem in eq. 4 from the global perspective. In those approaches,
the robots are essentially reduced to mobile sensors whose data is collected and processed
on a single computer. Examples of techniques solving C-SLAM from the global perspec-
tive include (Andersson and Nygards, 2008; Kim et al., 2010) that gather all the robots’
measurements at a central station to compute the global map. (L´azaro et al., 2013) im-
proves this solution by marginalizing unnecessary nodes in the local pose graphs so only
a few condensed measurements need to be shared to the central computer. Other central-
ized approaches (Forster et al., 2013; Schmuck and Chli, 2017; Schmuck and Chli, 2019)
perform C-SLAM with monocular cameras, successfully solving the associated 3D estima-
tion challenges, while (Loianno et al., 2015) focuses on micro-aerial vehicles constraints.
(Deutsch et al., 2016) proposes a framework to reuse existing single robot SLAM solutions
for C-SLAM. The same idea is explored in (Li et al., 2018), in which a popular single-robot
SLAM technique (Mur-Artal and Tard´os, 2017) is converted into C-SLAM. (Karrer et al.,
2018; Karrer and Chli, 2018) integrate inertial measurements from IMUs in their centralized
C-SLAM systems. (Jim´enez et al., 2018) proposes that the central node spreads the resulting
map across the robots to limit the memory usage.
Improving upon the centralized methods, some techniques do not rely on a single computer,
but can use diﬀerent robots or base stations for the computation. This way, the system
can adapt itself to the failure of one node or communication link and complete the mission.
Some decentralized systems solve the C-SLAM problem from the global perspective and
provide a single estimate comprised of data from the whole robotic team, with a typical
solution being replicated servers among the robots (Bailey et al., 2011). Alternatively, the
full mapping data is sent to every robot for redundancy and a subset of robots is designated
for computation (Saeedi et al., 2011a; Bresson et al., 2013; Saeedi et al., 2015).
C-SLAM from the Local Perspective Solving the C-SLAM problem from the local
perspective is radically diﬀerent, but oﬀers major beneﬁts in terms of computation, commu-
nication and privacy (Choudhary et al., 2017a; Cieslewski et al., 2018). Such systems are
usually distributed and solve the estimation problem from eq. 4 partially on each robot. As
shown in Figure 4b, each robot computes its own local map and uses partial information
from other robots as well as inter-robot measurements to achieve a local solution. Over
several iterations with its neighbors, each robot’s resulting local solution converges to a so-
lution consistent with the global reference frame. These techniques mitigate communication
and computation bottlenecks since the loads are spread across the robot team (Pﬁngsthorn
et al., 2008). As one would expect, distributed and local techniques come with many addi-
tional challenges that need to be tackled such as complex bookkeeping, information double
counting or synchronization issues.
3.4 C-SLAM architecture
In C-SLAM, as well as in single-robot SLAM, the front-end handles perception-related tasks
and the back-end generates state estimates using all measurements gathered. However,
in C-SLAM, the front-end and back-end computations do not necessarily occur fully on a
single robot anymore depending on the sensing, communication, and estimation strategies.
For example, in a centralized system, all robots could send their sensor data directly to a
single unit which would then perform the front-end and back-end steps for the whole team.
While in a decentralized and distributed setup, a robot could perform feature extraction on
its own and communicate with other robots for data association and state estimation. Every
part of a C-SLAM system can be subject to distribution or decentralization.
In addition, the front-end needs to ﬁnd links and relative measurements between the in-
dividual maps. Therefore, the front-end must also perform data association to detect and
compute inter-robot loop closures, which will be detailed in Section 4. It follows that the
back-end must generate estimates combining the individual and shared measurements as
described in Section 5.
4 C-SLAM Front-End
Although the division between the front-end and the back-end is sometimes blurry due to
the presence of feedback loops between the two processes, a typical C-SLAM front-end is in
charge of producing landmark estimates, odometry measurements, and both intra-robot and
inter-robot loop closures.
Odometry measurements aim to capture the translation and rotation of a robot from one
time step to the next. Common techniques measure wheel movements, integrate from an
IMU, and/or perform geometric matching between consecutive images or laser-scans. Intra-
robot loop closures are the measurements used by a SLAM system to relocalize itself and
reduce its estimate error caused by odometry drift. Using place recognition, the system can
detect previously visited locations and compute relative measurements between them. In
other words, intra-robot loop closures are estimates relating non-consecutive poses in the
robot trajectory that observed the same places. Since the computing of odometry and intra-
robot loop closure measurements can be fully done locally on each robot, the approaches
used are the same in both SLAM and C-SLAM. Thus, we refer the reader to (Mohamed
et al., 2019; Cadena et al., 2016; Lowry et al., 2016) for surveys of the current techniques.
Conversely, inter-robot loop closures relate poses of diﬀerent robots trajectories. They are
the seams that stitch together the estimates from multiple robots: they draw connections
between the individual robots’ local maps to build the global understanding of the environ-
ment. Generating inter-robot loop closures is the main focus of contributions to the front-end
of C-SLAM systems, and key to ensure consistency of the estimates.
4.1 Direct vs Indirect Inter-Robot Loop Closures Measurements
Inter-robot loop closures can be classiﬁed as direct or indirect (Kim et al., 2010). Direct
inter-robot loop closures occur when two robots meet, and they are able to estimate their
current relative location with respect to each other through direct sensing. Indirect inter-
robot loop closures are produced by looking back into maps to ﬁnd partial overlaps for
places that have been visited by both robots. Given these measurements, the robots can
estimate the relative transformation between their maps. In general, indirect inter-robot
loop closures detection produce more measurements and usually achieve a higher accuracy,
but require more communication and processing. Indeed, the detection process is often the
communication bottleneck of C-SLAM given the large amount of data required to compare
landmarks between the individual local maps (Tardioli et al., 2015).
4.1.1 Direct Inter-Robot Loop Closures
The idea of direct inter-robot loop closures is to compute the relative pose between two or
more robots when they physically meet in the same location. This is usually done through
direct sensing of one another. For example, (Kim et al., 2010) operated a quadcopter and
a ground robot and the latter was equipped with a checkerboard pattern that could be
detected by the quadcopter’s camera. (Zhou and Roumeliotis, 2006) used a combination
of direct and indirect detection approaches, where colored cylinders were installed to be
detected by omnidirectional cameras. In addition, (Gentner et al., 2018; Boroson et al.,
2020; Cao and Beltrame, 2020) propose to replace visual loop closures by Ultra-Wide Band
(UWB) measurements from beacons onboard the robots. Given a few distance measurements
provided by the UWB sensors, the robots can estimate their current relative pose with respect
to each other and establish a common reference frame.
4.1.2 Indirect Inter-Robot Loop Closures
Indirect inter-robot loop closure detection is the extension of single-robot loop closure de-
tection to multiple maps. In fact, approaches to ﬁnd indirect inter-robot loop closures often
rely on the same core algorithms as intra-robot loop closures. The ﬁrst challenge is the
loop closures detection, which consists of detecting overlaps between the individual maps.
This task is usually handled by a place recognition module which can eﬃciently compare
new observations against previous sections of the robots’ maps. Following place recognition
matches, geometric estimation is performed to compute the relative pose between the two
In the case of visual sensors, the place recognition problem has been studied extensively
(Lowry et al., 2016). The seminal work of visual bags of binary words (Galvez-L´opez and Tar-
dos, 2012) is still popular, but newer approaches based on deep learning, such as NetVLAD
(Arandjelovi´c et al., 2018), are more accurate and data-eﬃcient. Loop closure relative pose
measurements can be computed using visual features matching and multi-view geometry
(Hartley and Zisserman, 2003).
Finding inter-robot overlaps is a harder task with 3D point clouds given the dense data that
need to be shared and the lack of expressive features to perform place recognition. To that
end, compact and robust global point cloud descriptors (Uy and Lee, 2018) can be relied
upon to compare point clouds for place recognition. Other approaches extract features
from the point cloud that can serve for place recognition while providing initial guesses
for later geometric alignments (Ebadi et al., 2021), or even directly compute loop closure
measurements (Dub´e et al., 2017a). While the classical Iterative Closest Point method
(Besl and McKay, 1992) is still commonly used in single robot SLAM to compute relative
pose measurements between two matching point clouds, it is not well suited for multi-robot
operation due to its reliance on a good initial guess, which is usually not available between
the robots’ local maps. Therefore, a common solution is to use submaps matching for both
stereo cameras (Schuster et al., 2015; Schulz et al., 2019) and lidars (Dub´e et al., 2017b;
Dubois et al., 2020b; Ebadi et al., 2021). During this process, multiple laser scans or 3D
point clouds are clustered into submaps which can in turn be registered more eﬃciently.
4.2 Map Representation
When designing large systems, the choices of map representation could aﬀect computation
load, memory usage, and communication bandwidth. First, it is important to note that an
explicit map is not always required. For example, when the sole objective is collaborative
localization, a feature map can be suﬃcient. In those cases, each robot locally tracks land-
marks, or features, and searches for correspondences in other robots’ feature maps to obtain
indirect inter-robot loop closure measurements. This way, the robots can operate in the
same reference frame without the computation and communication burden of building an
interpretable map model.
When required, the chosen map representation depends on the mission objective and en-
vironment. For example, in the case of ground robots in ﬂat indoor environments, a 2D
map might be suﬃcient (Caccavale and Schwager, 2018). In those scenarios, occupancy grid
maps have been shown to be a compact and more accurate solution (Martin and Emami,
2010; Saeedi et al., 2011a) than feature-based maps (Benedettelli et al., 2010). However, 3D
representations are sometimes required (e.g. for rough terrain navigation) at the cost of more
computation, storage, and communication, which can be diﬃcult to handle when resources
are limited on the robots. Given the communication constraints in C-SLAM systems, com-
pact or sparse representations, such as topological maps (H. Jacky Chang et al., 2007; Saeedi
et al., 2014), are often preferred. In the same vein, some works aim for semantic-based rep-
resentations in the form of sparse maps of labelled regions (Choudhary et al., 2017b). Map
representations can also aﬀect long-term operations due to the increasing size of the map in
memory (Zhang et al., 2018a), which is also a challenge in single-robot SLAM.
4.3 Eﬃcient and Robust Communication
One of the core implementation diﬀerences between SLAM and C-SLAM is the need for
communication and coordination within the robotic team. For eﬃciency, the required band-
width needs to be minimal, and the communication network needs to be robust to robot
failures and diﬀerent topologies.
The exchanges of sensor data or representations relied upon to compute the inter-robot loop
closures (Section 4.1.2) are generally the communication bottleneck of a C-SLAM system
(Tardioli et al., 2015). Robots need to share enough data to detect if other robots have
visited the same area, and then estimate a map alignment using overlaps of their maps.
Hence, contributions to the front-end of C-SLAM systems often consist in mechanisms to
perform the search eﬃciently over a team considering communication constraints.
4.3.1 Eﬃcient Data Sharing
While some early techniques simply share all the data from one robot to another, new sensors
produce increasingly rich and dense data. The days of raw sensor data transmission are over
and most current techniques in the literature opt for some sort of compression or reduction.
Even among the early techniques (Nettleton et al., 2006), the idea of a communication budget
has been explored. More recently, the topic has gathered more attention with new techniques
carefully coordinating the exchange of data when two robots meet each other accounting for
the available communication and computation resources (Giamou et al., 2018; Tian et al.,
2018a; Tian et al., 2018b; Tian et al., 2020a). One idea is to compress generated maps using
self-organizing maps obtained through unsupervised learning (Saeedi et al., 2011b; Best and
Hollinger, 2020). The use of compact representations has also been explored with high-
level semantic features: (Choudhary et al., 2017b) relies on objects as landmarks, needing
to communicate only object labels and poses to other robots, and (Ramtoula et al., 2020)
presents a compact object-based descriptor relying on the conﬁguration of objects in a scene
to perform place recognition. In addition to making representations compact, it is useful to
ensure that only helpful information is shared. Hence, (Kepler and Stilwell, 2020) introduces
a novelty metric so that only suﬃciently novel measurements compared to the existing map
The problem has been extensively studied speciﬁcally for visual C-SLAM: (Tardioli et al.,
2015) proposes to share visual vocabulary indexes instead of feature descriptors to reduce
the required bandwidth. Other approaches focus on scalable team-wide place recognition by
assigning each robot with a predetermined range of words from a pretrained visual bag of
words (Cieslewski and Scaramuzza, 2017b), or regions of full-image descriptors (Cieslewski
and Scaramuzza, 2017a). (Dymczyk et al., 2015; Contreras and Mayol-Cuevas, 2017) re-
move landmarks that are not necessary for localization, (Opdenbosch and Steinbach, 2019)
introduces a new coding to eﬃciently compress features, and (Dubois et al., 2019) proposes
data sharing algorithms specialized for visual inertial C-SLAM.
In some extreme cases, communication is severely limited due to the properties of the trans-
mission medium or the large distance between the robots: (Paull et al., 2014; Paull et al.,
2015) explore the special case of underwater operations with low bandwidth acoustic com-
munication, and (Schulz et al., 2019) considers long distance radio modules with very limited
bandwidth to build the collaborative map through small incremental updates.
4.3.2 Network Topology
Another important aspect to consider is the network topology. Current techniques either
assume full connectivity, multi-hop connectivity or are rendezvous-based. Full connectivity
means that each robot can directly communicate with all other robots at any time such
as in (Cieslewski and Scaramuzza, 2017a; Cieslewski and Scaramuzza, 2017b). Multi-hop
connectivity implies that robots can only share information with their neighbors and it might
require multiple neighbor-to-neighbor transmissions to reach all robots (Arag¨u´es et al., 2010;
Montijano et al., 2013). Rendezvous-based communication means that the robots share
data only when they meet and, therefore, do not require any connectivity maintenance.
Rendezvous-based C-SLAM also oﬀers the opportunity to perform direct inter-robot loop
closure detection during the encounters (Zhou and Roumeliotis, 2006).
The impact of the network topology is especially important during the inference step because
disconnections or multi-hop paths can lead to inconsistencies or synchronization issues. Thus,
(Leung et al., 2011b; Leung et al., 2012) examine the conditions that allow distributed
inference to reach the same result as a centralized equivalent approach. Another approach
(Quraishi et al., 2016) leverages the progress in the ﬁeld of distributed computing to improve
the robustness to connectivity losses, while (Tuna et al., 2015) evaluates the use of Wireless
Sensor Network-based communication which is less reliable and predictable, but oﬀers a
ﬂexible architecture with self-organization capabilities.
4.4 Heterogeneous Sensing
In many applications, the teams of robots are composed of diﬀerent platforms equipped
with diﬀerent onboard sensors. Heterogeneous sensing comes with the additional challenge
of matching map data in diﬀerent representation to perform relocalization and/or map fu-
sion. To this end, a recent study evaluated the repeatability of existing keypoint detectors
between data from stereo cameras and lidars For example, when matching data from both
stereo cameras and lidars, one needs to chose repeatable 3D feature representations that are
consistent despite the diﬀerences in density and ﬁeld-of-view (Boroson and Ayanian, 2019).
Another approach is to use an intermediate map representation that can be produced by dif-
ferent kinds of sensors (Koch and Lacroix, 2016). For example, (K¨aslin et al., 2016) proposes
to compare elevation maps that are invariant to sensor choice: lidars or cameras.
4.5 Non-Conventional Sensing
While most C-SLAM techniques use the typical SLAM sensors (i.e., lidars and monoc-
ular, RGB-D, or stereo cameras), many recent research works have explored the use of
non-conventional sensors: (Choi et al., 2014) uses omnidirectional (i.e., ﬁsh-eye) cameras,
(Waniek et al., 2015) performs C-SLAM with event-based vision sensors, and (Morales and
Kassas, 2018) integrates ambient radio signals (i.e., signals of opportunity) into their system.
In a similar vein, (Liu et al., 2020) leverages existing WiFi access points in most indoor en-
vironments to perform loop closures based on their radio signal ﬁngerprint. There is also an
ongoing trend of leveraging semantic information obtained through deep learning to perform
loop closure detection as explored in Section 7.2.
5 C-SLAM Back-End
As mentioned before, the role of the C-SLAM back-end is to estimate the state of the robot
and the map given the front-end measurements. The diﬀerence with single-robot SLAM is
the presence of inter-robot measurements, the need to reach consensus, and the potential
lack of an initial guess since the global reference frame and the starting positions of the
robots are usually initially unknown. Nevertheless, similar to single-robot solvers, C-SLAM
back-ends are roughly divided in two main categories of inference techniques: ﬁltering-based
and smoothing-based. Although ﬁltering-based approaches were the most common among
the early techniques (e.g. EKF (Rekleitis et al., 2003) and particle ﬁlters (Madhavan et al.,
2004)), smoothing-based approaches quickly gained in popularity and are currently con-
sidered as superior in most applications (Strasdat et al., 2012). This section provides an
overview of the diﬀerent categories of estimation workhorses for C-SLAM and presents ex-
amples from the literature.
5.1 Filtering-Based Estimation
Filtering approaches are often characterized as online in the sense that only the current robot
pose is estimated and all previous poses are marginalized out (Thrun et al., 2005) at each
time step. Consequently, the estimation of the posterior in eq. 1 at time tonly depends on
the posterior at time t−1 and the new measurements.
The classical ﬁltering technique for nonlinear problems (i.e., all problems in robotics except
trivial ones) is the Extended Kalman Filter (EKF). It has been applied to C-SLAM in various
ways among which the information ﬁlter method presented in (Thrun and Liu, 2005). In a
nutshell, EKF are Gaussian ﬁlters that circumvent the linear assumptions of Kalman ﬁlters
through linearization (i.e., local linear approximation); however, the linearization process
potentially leads to inconsistencies when the noise is too large. A major advantage of EKF
techniques (Thrun and Liu, 2005; Sasaoka et al., 2016; Luft et al., 2016; Schuster et al.,
2019) over smoothing techniques is that the covariance matrix is available without requiring
additional computation, which can be useful for feature tracking or active exploration. For
example, one could prioritize the exploration in the most uncertain directions. Yet, an
explicit covariance matrix is rarely required, so alternative ﬁltering techniques seek to avoid
its computation, such as the smooth variable structure ﬁlters approach presented in (Demim
et al., 2017).
Building on the EKF, Rao-Blackwellized Particle Filters (RBPF) (Doucet et al., 2000) are
another popular ﬁltering approach for the C-SLAM problem. Techniques, such as (Howard,
2006), use samples (particles) to represent the posterior distribution in eq. 1 and perform
variable marginalization using an EKF which drastically reduce the size of the sampling
space. (Carlone et al., 2011) extends on (Howard, 2006) and improves its consistency while
making it fully distributed. (Gil et al., 2010) adapts RBPF to visual C-SLAM and (D¨orr
et al., 2016) showcases the potential of RBPF C-SLAM for industrial applications.
5.2 Smoothing-Based Estimation
Besides the linearization error, another drawback of ﬁltering techniques is that the marginal-
ization of past pose variables leads to many new links among the remaining variables. Indeed,
the elimination of each pose variable leads to interdependence between every landmark vari-
ables to which it was connected. As a result, the variables become increasingly coupled
and this leads to more computation. However, in smoothing, there is less marginalization re-
quired which means that the variables will stay sparsely connected. This sparsity is exploited
by modern solvers to yield signiﬁcant speed-ups (Strasdat et al., 2012). In addition, unlike
ﬁltering-based approaches, smoothing techniques improve their accuracy by revisiting past
measurements instead of only working from the latest estimate. Hence, ﬁltering techniques
fell out of favor due to the better performance of smoothing both in terms of accuracy and
eﬃciency (Strasdat et al., 2012). Moreover, in the context of C-SLAM, the sparsity reduces
the amount of data to be exchanged during the estimation process (Paull et al., 2015).
In the following, we present a general smoothing formulation for pose-graph C-SLAM with
two robots (α, β) in which the map landmarks are marginalized into odometry and loop
First, assuming that the measurements noises are uncorrelated, we can factorize eq. 4 as
p(Zα, Zβ, Zαβ |Xα, Xβ)
α) is the likelihood of measurement zi
αgiven the subset of variable Xi
it is solely dependent, p(zj
β) is the likelihood of measurement zj
βgiven the subset of
βon which it is solely dependent, p(zk
β) is the likelihood of measurement
αβ given the subset of variable Xk
β. There are lmeasurements related only to state
variables from robot α,mmeasurements related only to state variables from robot β, and n
measurements related to state variables from both robots.
Second, assuming that the measurements are disturbed by zero-mean Gaussian noise with
information matrix Ω (i.e., inverse of the covariance), we can express the individual mea-
surement likelihood as
αis a function that maps the state variables to the measurements.
Third, since maximizing the likelihood is equivalent to minimizing the negative log-likelihood,
we obtain the following nonlinear least squares formulation of problem 4:
This nonlinear least squares problem can be solved either on a single computer or in a
distributed fashion. In the centralized case, one can simply use single-robot pose graph
optimization solvers (Agarwal et al., ; K¨ummerle et al., 2011; F. Dellaert et al., ; Rosen et al.,
2019). Incremental single-robot solvers can also be adapted for the centralized C-SLAM
problem to continuously update the global pose graph with the latest measurements from
the robots (Dong et al., 2015).
Among the distributed solvers, many early techniques used Gaussian elimination (Cunning-
ham et al., 2010; Cunningham et al., 2013; Cunningham et al., 2012). Although popular,
those approaches require the exchange of dense marginals which means that the communi-
cation cost is quadratic on the number of inter-robot measurements. Furthermore, those
approaches rely on linearization, so they require complex bookkeeping to ensure the con-
sistency at the linearization point within the team of robots. To reduce the complexity,
(Nerurkar et al., 2009) introduces a distributed marginalization scheme to limit the size of
the optimization problem.
More recently, the approach in (Choudhary et al., 2017a) leverages the Distributed Gauss-
Seidel algorithm introduced in (Bertsekas and Tsitsiklis, 1989) to solve eq. 7. This technique
avoids complex bookkeeping and information double-counting in addition of satisfying pri-
vacy constraints by exchanging minimal information on the robot individual trajectories.
Riemannian gradient descent has also been considered extensively to solve the C-SLAM
problem (Knuth and Barooah, 2012; Knuth and Barooah, 2013). Approaches in (Tron and
Vidal, 2009; Tron and Vidal, 2014; Tron et al., 2016) introduce a multi-stage distributed
Riemannian consensus protocol with convergence guarantees to globally optimal solutions in
noiseless scenarios. Expanding on those ideas, a recent technique (Tian et al., 2021), based
upon a sparse semideﬁnite relaxation, provides exactness guarantees even in the presence of
moderate measurement noise. Moreover, this latter technique has been extended to consider
asynchronous scenarios and parallel computation (Tian et al., 2020b), which are often critical
to deal with communication delays inherent to multi-robot systems.
5.3 Other Estimation Techniques
Other estimation techniques have been proposed for C-SLAM. Among them, the distributed
Jacobi approach has been shown to work for 2D poses (Aragues et al., 2011). (Franceschelli
and Gasparri, 2010; Aragues et al., 2012) look into consensus-based algorithms and prove
their convergence across teams of robots. Also, apart from the solver itself, researchers have
studied which measurement and noise models are the best suited for C-SLAM (Indelman
et al., 2012).
We observe that more exciting new directions are still being discovered, considering that re-
cent approaches such as (Tian et al., 2021) have been shown to outperform, both in accuracy
and convergence rate, the well established Distributed Gauss-Seidel pose graph optimization
method (Choudhary et al., 2017a) reused in many state-of-the-art C-SLAM systems such as
(Cieslewski et al., 2018; Lajoie et al., 2020; Wang et al., 2019). Those promising approaches
also include the majorization-minimization technique from (Fan and Murphey, 2020) and
the consensus-based 3D pose estimation technique inspired by distributed formation control
from (Cristofalo et al., 2019; Cristofalo et al., 2020).
5.4 Perceptual Aliasing Mitigation
As it is the case in single robot SLAM, loop closure detection is vulnerable to spurious mea-
surements, i.e., outliers, due to perceptual aliasing (S¨underhauf and Protzel, 2012; Agarwal
et al., 2013; Latif et al., 2013; Lajoie et al., 2019). This phenomenon occurs when two dif-
ferent places are conﬂated as the same during the place recognition process. This motivates
the need for robust techniques that can detect and remove those outliers to avoid dramatic
distortions in the C-SLAM estimates. Outliers mitigation might also help against adversarial
attacks by rejecting spurious measurements injected by a nefarious agent.
The classic approach to remove outliers is to use the RANSAC algorithm (Fischler and Bolles,
1981) to ﬁnd a set of mutually consistent measurements (Dong et al., 2015). While RANSAC
works well in centralized settings, it is not adapted to distributed systems. Therefore, re-
searchers recently explored other ways of detecting outliers such as leveraging extra infor-
mation from the wireless communication channels during a rendezvous between two robots
(Wang et al., 2019). Since such approaches work only for direct inter-robot loop closures,
there is a need for general robust data association in the back-end. To that end, (Indelman
et al., 2014) uses expectation maximization to infer which inter-robot measurements are in-
liers and which ones are outliers. However, probably the most popular approach currently
in C-SLAM is the use of pairwise consistency maximization to search for the maximal clique
of pairwise consistent measurements among the inter-robot loop closures (Mangelson et al.,
2018). (Lajoie et al., 2020) introduces a distributed implementation of this technique which
does not require any additional communication when paired with distributed pose graph op-
timization, while (Chang et al., 2020) proposes an incremental version, and (Do et al., 2020)
extends the pairwise consistency evaluation with a data similarity metric. It is important
to note that those latest approaches only apply to smoothing-based C-SLAM since, unlike
ﬁltering, it allows the removal of past measurements from the estimation.
6 Benchmarking C-SLAM
Despite the tremendous progress in the ﬁeld during the last decade, C-SLAM techniques face
tough challenges in terms of reproducibility and benchmarking. C-SLAM systems involve
multiple software modules and lots of diﬀerent hardware components, making it hard to
replicate perfectly. While standardized benchmarking approaches have been emerging for
single-robot SLAM (Bujanca et al., 2019), such systematic evaluation techniques are still
lacking for C-SLAM.
Moreover, only a few datasets dedicated to C-SLAM exist. (Leung et al., 2011a) consists of
9 monocular camera subdatasets and (Dubois et al., 2020a) is dedicated to stereo-inertial
C-SLAM. Therefore, the common approach to evaluate C-SLAM solutions is to split single
robot SLAM datasets into multiple parts and to associate each one to a robot. When splitting
the dataset, careful attention has to be given to ensure the presence of overlaps between the
parts for loop closing. In addition, one should avoid overlaps near the cutting points, where
the viewpoint and lighting conditions are exactly the same since they depict the same place
viewed by the robot at the same point in time: this kind of overlaps is highly unrealistic in
multi-robot operations. One of the most used dataset in the literature is the KITTI self-
driving car dataset comprised of lidar and stereo camera data (Geiger et al., 2012). New
datasets of interest include KITTI360 (Xie et al., 2016) which adds ﬁsh-eye cameras and the
very large Pit30M lidar and monocular camera dataset that contains over 30 million frames
(Martinez et al., 2020).
7 Ongoing and Future Trends
This section presents trending ideas in the research community to improve C-SLAM. Those
new trends push the boundaries of what C-SLAM can do and oﬀer an exciting view of the
7.1 Active C-SLAM
The concept of active SLAM comes from the powerful idea that while SLAM naturally
improves path planning and control, those can also improve SLAM. In C-SLAM, gains can
also be made by leveraging the coordination between the mapping robots. Having feedback
loops to the C-SLAM algorithm allows path planning optimization for faster coverage and
mapping of the environment (Bryson and Sukkarieh, 2007; Bryson and Sukkarieh, 2009). To
achieve those goals, (Mahdoui et al., 2020) aims to minimize the global exploration time and
the average travelled distance. Other examples of the coupling between path planning and
SLAM include (Trujillo et al., 2018) which shows the advantages of UAVs ﬂying in formation
for monocular C-SLAM, and (Pei et al., 2020) which uses deep Q-learning to decide whether
a robot should localize the others or continue exploring on its own.
Active C-SLAM can also increase the estimation accuracy. To that end, (Dinnissen et al.,
2012) uses reinforcement learning to determine the best moment to merge the local maps,
and (Kontitsis et al., 2013) leverages instead the covariance matrix computed by the EKF-
based inference engine to select trajectories that reduces the map uncertainty. Similarly,
(Atanasov et al., 2015) develops a theoretical approach to design a sensor control policy
which minimizes the entropy of the estimation task, while (Chen et al., 2020) proposes to
broadcast the weakest nodes in the C-SLAM pose graph topology to actively increase the
7.2 Semantic C-SLAM
With the rise of deep learning and its impressive semantic inference capabilities, a lot of in-
terest have been directed towards semantic mapping in which the environment is interpreted
using class labels (i.e., person, car, chair, etc.).
This idea was ﬁrst applied to C-SLAM in (Wu et al., 2009) which detects blobs of colors
as salient landmarks in the robots maps. (Choudhary et al., 2017b) later leverages deep
learning-based object detection to perform object-based C-SLAM. Representing maps as
a collection of objects is very compact and therefore well suited for systems with tight
communication constraints. However, those object-based techniques rely heavily upon the
presence of many objects of the known classes in the environment (i.e., classes in the training
data). Thus, they do not generalize well to arbitrary settings.
The other preferred approach for semantic C-SLAM is to annotate maps of the environment
with class labels. For example, (Frey et al., 2019; Ramtoula et al., 2020) use constellations
of landmarks each comprised of a 3D point cloud, a class label and an appearance descriptor.
(Tchuiev and Indelman, 2020) considers the joint estimation of object labels and poses in
addition to the robots poses. (Chang et al., 2020) builds globally consistent local metric
maps that are enhanced with local semantic labelling.
7.3 Dynamic Environments
Another inherent problem in multi-robot system is the presence of moving objects in the
environment (e.g. people, vehicles, or other moving robots). This is a substantial issue since
SLAM techniques rely on the tracking of static landmarks. To solve this problem, (Lee and
Lee, 2009) proposes the simple idea of pointing the cameras towards the ceiling when oper-
ating indoors with ground robots. (Zou and Tan, 2013) proposes instead to classify dynamic
points using the reprojection error and to keep only the static points for estimation. In a
diﬀerent vein, (Moratuwage et al., 2013; Moratuwage et al., 2014; Battistelli et al., 2017) and
more recently (Gao et al., 2020) extend upon the Rao-Blackwellized particle ﬁlters frame-
work to track moving features and remove them from the estimation process. Those works
use Random-Finite-Sets which were originally developed for multi-target tracking. This
way, they manage to incorporate data association, landmark appearance and disappearance,
missed detections, and false alarms in the ﬁltering process.
7.4 Cloud Robotics
Recent research suggest that C-SLAM could eﬃciently leverage the recent progress in cloud
computing. The connection between the two ﬁelds is somewhat intuitive. Why perform
all the processing on robots with limited resources when we could use powerful remote
clusters of servers instead? For example, (Riazuelo et al., 2014) oﬄoads the expensive map
optimization and storage to a server in the cloud. (Yun et al., 2017) proposes a cloud robotics
framework for C-SLAM based on available commercial platforms. Using a similar approach,
(Zhang et al., 2018b) manages to perform C-SLAM with up to 256 robots. This is orders of
magnitude more than the current techniques based on onboard computation can achieve.
However, while cloud techniques solve the problem of limited computing power onboard the
robots, they still face the issue of limited communication bandwidth which is exacerbated
when many robots transmit their data through a single internet link. Hence, instead of using
remote servers, (Gouveia et al., 2015) proposes to use a subset of a team of robots to act as
a computing cluster to free other robots from the heavy computation burden. Such moving
clusters performing computing closer to the sources of data are in accordance with the edge
computing paradigm (Satyanarayanan, 2017) to save bandwidth and reduce response time.
7.5 Augmented Reality
Apart from the well known UAV or self-driving cars applications, Augmented Reality (AR)
is probably one of the biggest ﬁeld of application of SLAM. Indeed, SLAM makes markerless
AR applications possible by building a map of the surrounding environment which is essential
to overlay digital interactive augmentations. In other words, SLAM is required to make
AR work in environments without motion capture, localization beacons or predetermined
markers. In the foreseeable future, AR applications and games will push for multi-agent
collaboration and this is where C-SLAM comes into play (Egodagamage and Tuceryan,
2017; Egodagamage and Tuceryan, 2018). To that end, (Morrison et al., 2016) proposes a
centralized approach in which virtual elements are shared by all agents, and (Sartipi et al.,
2019) introduces a decentralized AR technique with smartphones, making use of the visual
and inertial sensors already present in those devices.
Some other techniques also look at the tremendous potential of AR for intuitive robotic
control. (Sidaoui et al., 2019) adds a human in the loop equipped with an AR system to
edit and correct the map produced by a robot during a mission. Interestingly, (Yu et al.,
2020) goes for an opposite approach in which humans, equipped with smartphones, map an
environment and get feedback from a central server to indicate which unscanned areas still
need to be explored.
In this paper, we presented the core ideas behind Collaborative Simultaneous Localization
and Mapping and provided a survey of existing techniques. First, we introduced the basic
concepts of a C-SLAM system. We provided explanations and bits of historical context to
better understand the astonishing progress recently made in the ﬁeld. Then, we presented the
building blocks of a typical C-SLAM system and the associated techniques in the literature.
We also touched upon the diﬃculties of reproducibility and benchmarking. Afterwards, we
explored new trends and challenges in the ﬁeld that will certainly receive a lot more interests
in the future. In summary, we focused on providing a complete overview of the C-SLAM
We have shown, through numerous examples, how C-SLAM systems are varied and need
to match closely the application requirements: sparse or dense maps, precise or topological
localization, the number of robots involved, the networking limitations, etc. We wish for this
survey to be a useful tool for C-SLAM practionners looking for adequate solutions to their
Nevertheless, despite the current growing interest for C-SLAM applications, it is still a young
topic of research and many fundamental problems have to be resolved before the advance
of C-SLAM-based commercial products. In particular, we believe that current systems scale
poorly and are often limited to very few robots. So, a lot of work is still required to achieve
large teams of robots building maps and localizing themselves collaboratively. We also
note the growing interest for semantic C-SLAM to make robotic maps more interpretable
and more actionable. Scene understanding techniques in the computer vision ﬁeld could
bring more compact and expressive environment representations into the SLAM system,
which potentially increase the map readability while reducing the communication burden.
Furthermore, the rise of AR, in conjunction with C-SLAM and semantics, will oﬀer incredible
opportunities of innovation in the ﬁelds of robotics, mobile sensing, and entertainment.
This work was partially supported by a Canadian Space Agency FAST Grant, a Vanier
Canada Graduate Scholarships Award, the Arbour Foundation, the EPSRC Centre for Doc-
toral Training in Autonomous Intelligent Machines and Systems [EP/S024050/1], and Oxbot-
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