1
Past, Present, and Future of Simultaneous
Localization And Mapping: Towards the
Robust-Perception Age
Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif,
Davide Scaramuzza, Jos
´
e Neira, Ian D. Reid, John J. Leonard
Abstract—Simultaneous Localization and Mapping (SLAM)
consists in the concurrent construction of a model of the
environment (the map), and the estimation of the state of the robot
moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world
applications, and witnessing a steady transition of this technology
to industry. We survey the current state of SLAM. We start by
presenting what is now the de-facto standard formulation for
SLAM. We then review related work, covering a broad set of
topics including robustness and scalability in long-term mapping,
metric and semantic representations for mapping, theoretical
performance guarantees, active SLAM and exploration, and
other new frontiers. This paper simultaneously serves as a
position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate
open challenges and new research issues, that still deserve careful
scientific investigation. The paper also contains the authors’ take
on two questions that often animate discussions during robotics
conferences: Do robots need SLAM? and Is SLAM solved?
Index Terms—Robots, SLAM, localization, mapping.
I. INTRODUCTION
S
LAM consists in the simultaneous estimation of the state
of a robot equipped with on-board sensors, and the con-
struction of a model (the map) of the environment that the
sensors are perceiving. In simple instances, the robot state is
described by its pose (position and orientation), although other
quantities may be included in the state, such as robot velocity,
sensor biases, and calibration parameters. The map, on the
other hand, is a representation of aspects of interest (e.g.,
position of landmarks, obstacles) describing the environment
in which the robot operates.
The need to build a map of the environment is twofold.
First, the map is often required to support other tasks; for
instance, a map can inform path planning or provide an
C. Cadena is with the Autonomous Systems Lab, ETH Zurich, Switzerland.
e-mail: cesarc@ethz.ch
L. Carlone is with the Laboratory for Information and Decision Systems,
Massachusetts Institute of Technology, USA. e-mail: lcarlone@mit.edu
H. Carrillo is with the Escuela de Ciencias Exactas e Ingenier
´
ıa, Universidad
Sergio Arboleda, Colombia. e-mail: henry.carrillo@usa.edu.co
Y. Latif and I.D. Reid are with the Australian Center for
Robotic Vision, University of Adelaide, Australia. e-mail:
yasir.latif@adelaide.edu.au, ian.reid@adelaide.edu.au
J. Neira is with the Departamento de Inform
´
atica e Ingenier
´
ıa de Sistemas,
Universidad de Zaragoza, Spain. e-mail: jneira@unizar.es
D. Scaramuzza is with the Robotics and Perception Group, University of
Zurich, Switzerland. e-mail: sdavide@ifi.uzh.ch
J.J. Leonard is with Marine Robotics Group, Massachusetts Institute of
Technology, USA. e-mail: jleonard@mit.edu
intuitive visualization for a human operator. Second, the map
allows limiting the error committed in estimating the state of
the robot. In the absence of a map, dead-reckoning would
quickly drift over time; on the other hand, given a map, the
robot can “reset” its localization error by re-visiting known
areas (the so called loop closure). Therefore, SLAM finds
applications in all scenarios in which a prior map is not
available and needs to be built.
In some robotics applications a map of the environment is
known a priori. For instance, a robot operating on a factory
floor can be provided with a manually-built map of artificial
beacons in the environment. Another example is the case in
which the robot has access to GPS data (the GPS satellites
can be considered as moving beacons at known locations).
The popularity of the SLAM problem is connected with the
emergence of indoor applications of mobile robotics. Indoor
operation rules out the use of GPS to bound the localization
error; furthermore, SLAM provides an appealing alternative
to user-built maps, showing that robot operation is possible in
the absence of an ad-hoc localization infrastructure.
A thorough historical review of the first 20 years of the
SLAM problem is given by Durrant-Whyte and Bailey in
two surveys [14, 94]. These mainly cover what we call the
classical age (1986-2004); the classical age saw the intro-
duction of the main probabilistic formulations for SLAM,
including approaches based on Extended Kalman Filters, Rao-
Blackwellised Particle Filters, and maximum likelihood esti-
mation; moreover, it delineated the basic challenges connected
to efficiency and robust data association. Other two excellent
references describing the three main SLAM formulations of
the classical age are the chapter of Thrun and Leonard [299,
chapter 37] and the book of Thrun, Burgard, and Fox [298].
The subsequent period is what we call the algorithmic-analysis
age (2004-2015), and is partially covered by Dissanayake et
al. in [89]. The algorithmic analysis period saw the study
of fundamental properties of SLAM, including observability,
convergence, and consistency. In this period, the key role of
sparsity towards efficient SLAM solvers was also understood,
and the main open-source SLAM libraries were developed.
We review the main SLAM surveys to date in Table I,
observing that most recent surveys only cover specific aspects
or sub-fields of SLAM. The popularity of SLAM in the last 30
years is not surprising if one thinks about the manifold aspects
that SLAM involves. At the lower level (called the front-end
in Section II) SLAM naturally intersects other research fields
arXiv:1606.05830v2 [cs.RO] 20 Jul 2016
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