Citation: Macario Barros, A.; Michel,
M.; Moline, Y.; Corre, G.; Carrel, F.
A Comprehensive Survey of Visual
SLAM Algorithms. Robotics 2022, 11,
24. https://doi.org/10.3390/
robotics11010024
Academic Editor: Dario Richiedei
Received: 12 December 2021
Accepted: 7 February 2022
Published: 10 February 2022
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Review
A Comprehensive Survey of Visual SLAM Algorithms
Andréa Macario Barros * , Maugan Michel, Yoann Moline , Gwenolé Corre and Frédérick Carrel
Laboratoire Capteurs et Architectures Électroniques (LCAE), Laboratoire d’Intégration des Systèmes et des
Technologies (LIST), Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA), 91400 Saclay, France;
maugan.michel@cea.fr (M.M.); yoann.moline@cea.fr (Y.M.); gwenole.corre@cea.fr (G.C.);
frederick.carrel@cea.fr (F.C.)
* Correspondence: andrea.barros@cea.fr; Tel.: +33-1-69-08-22-59
Abstract:
Simultaneous localization and mapping (SLAM) techniques are widely researched, since
they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown
environment. Visual-based SLAM techniques play a significant role in this field, as they are based on
a low-cost and small sensor system, which guarantees those advantages compared to other sensor-
based SLAM techniques. The literature presents different approaches and methods to implement
visual-based SLAM systems. Among this variety of publications, a beginner in this domain may find
problems with identifying and analyzing the main algorithms and selecting the most appropriate
one according to his or her project constraints. Therefore, we present the three main visual-based
SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main
algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages
and disadvantages of each technique. Furthermore, we propose six criteria that ease the SLAM
algorithm’s analysis and consider both the software and hardware levels. In addition, we present
some major issues and future directions on visual-SLAM field, and provide a general overview of
some of the existing benchmark datasets. This work aims to be the first step for those initiating a
SLAM project to have a good perspective of SLAM techniques’ main elements and characteristics.
Keywords:
embedded SLAM; evaluation criteria; RGB-D SLAM; visual-inertial SLAM; visual-SLAM;
3D reconstruction
1. Introduction
Simultaneous localization and mapping (SLAM) technology, first proposed by Smith
in 1986 [
1
], is used in an extensive range of applications, especially in the domain of
augmented reality (AR) [
2
–
4
] and robotics
[5–7].
The SLAM process aims at mapping an
unknown environment and simultaneously locating a sensor system in this environment
through the signals provided by the sensor(s). In robotics, the construction of a map is a
crucial task, since it allows the visualization of landmarks, facilitating the environment’s
visualization. In addition, it can help in the state estimation of the robot, relocating it,
and decreasing estimation errors when re-visiting registered areas [8].
The map construction comes with two other tasks: localization and path planning.
According to Stachniss [
9
], the mapping problem may be described by examining three
questions considering the robot’s perspective: What does the world look like? Where am I?
and How can I reach a given location? The first question is clarified by the mapping task,
which searches to construct a map, i.e., a model of the environment. To do so, it requires
the location of the observed landmarks, i.e., the answer for the second question, provided
by the localization task. The localization task searches to determine the robot’s pose, i.e., its
orientation and position and, consequently, locates the robot on the map. Depending on
the first two tasks, the path planning clears up the last question, and seeks to estimate a
trajectory for the robot to achieve a given location. It relies on the current robot’s pose,
provided by the localization task, and on the environment’s characteristics, provided by the
mapping task. SLAM is a solution that integrates both the mapping and localization tasks.
Robotics 2022, 11, 24. https://doi.org/10.3390/robotics11010024 https://www.mdpi.com/journal/robotics
