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.
Highlights
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) [2,3,4] and robotics [5,6,7]
This article presents three main contributions: 1—An explanation of the most representative visual-based SLAM algorithms through the construction of diagrams and flowcharts. This approach will be helpful to the reader, as it provides an overview of the SLAM techniques when initiating a project and allows the reader to have a first contact with the visual-based SLAM algorithms. 2—As far as we know, this is the first review article that presents the three main visual-based approaches, performing an individual analysis of each method and a general analysis of the approaches. 3—Focusing on the readers initiating their studies on the SLAM algorithms, we propose six main criteria to be observed in the different techniques and implementations to be considered according to one’s application
The visual-based SLAM techniques represent a wide field of research thanks to their robustness and accuracy provided by a cheap and small sensor system
Summary
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) [2,3,4] and robotics [5,6,7]. The construction of a map is a crucial task, since it allows the visualization of landmarks, facilitating the environment’s visualization. It can help in the state estimation of the robot, relocating it, and decreasing estimation errors when re-visiting registered areas [8]. 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
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