Advances in Visual Simultaneous Localisation and Mapping Techniques for Autonomous Vehicles: A Review

Abstract

The recent advancements in Information and Communication Technology (ICT) as well as increasing demand for vehicular safety has led to significant progressions in Autonomous Vehicle (AV) technology. Perception and Localisation are major operations that determine the success of AV development and usage. Therefore, significant research has been carried out to provide AVs with the capabilities to not only sense and understand their surroundings efficiently, but also provide detailed information of the environment in the form of 3D maps. Visual Simultaneous Localisation and Mapping (V-SLAM) has been utilised to enable a vehicle understand its surroundings, map the environment, and identify its position within the area. This paper presents a detailed review of V-SLAM techniques implemented for AV perception and localisation. An overview of SLAM techniques is presented. In addition, an in-depth review is conducted to highlight various V-SLAM schemes, their strengths, and limitations. Challenges associated with V-SLAM deployment and future research directions are also provided in this paper.

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization

“…However, it faces challenges such as large-scale scenes, numerous dynamic objects, intense lighting variations, and rapid movements [ 2 ]. To address the emerging challenges posed by intelligent vehicles, scholars have conducted research from multiple perspectives including the front-end, back-end, and vision-based multi-sensor fusion [ 1 ].…”

“…With the development of intelligent vehicles, the demand for environmental perception and precise localization is increasing. Visual Simultaneous Localization and Mapping (SLAM), as a vision-based localization and mapping method, holds broad application prospects in intelligent vehicles [ 1 ]. Compared to GPS-based methods, stereo Visual SLAM can achieve stable localization and mapping in GNSS-denied scenes, providing precise and stable localization for autonomous driving [ 1 , 2 ].…”

“…Visual Simultaneous Localization and Mapping (SLAM), as a vision-based localization and mapping method, holds broad application prospects in intelligent vehicles [ 1 ]. Compared to GPS-based methods, stereo Visual SLAM can achieve stable localization and mapping in GNSS-denied scenes, providing precise and stable localization for autonomous driving [ 1 , 2 ]. Compared to Lidar-based SLAM, stereo cameras can similarly acquire precise scales of scenes at lower costs.…”

Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science

“…The production of precise maps of fields and orchards is an important use of GNSS-based SLAM in PA [97]. GNSS-based SLAM may also be utilized for precise agricultural machinery guiding [98]. As irrigation is another important part of agriculture, precision irrigation may assist in minimizing water use while boosting crop yields.…”

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

“…In static situations, landmarks stay stationary, but in dynamic environments, landmarks change location [35]. In this research, two main challenges have been highlighted regarding V-SLAM's applicability [36]:…”