Stereo Visual SLAM for Autonomous Vehicles: A Review

Abstract

Simultaneous Localization and Mapping (SLAM) problem, where an autonomous vehicle moving in an unknown environment attempts to sense and map its surroundings while recognizing its own location and trajectory within the map, has always been a notable and popular research topic in the field of computer vision, robotics and artificial intelligence. Among the various types of solutions relying on different sensor modalities such as the global positioning system (GPS), radio signals, lidar, etc., vision-based solutions are of major interest nowadays because most cameras are low-cost and rich information gathering, especially for the stereo cameras. In this paper, different technologies of visual SLAM, where the main sensors are cameras, are surveyed with an emphasis on methodologies using stereo cameras. Some state-of-the-art open-source stereo visual SLAM frameworks are also discussed and compared. Finally, a general discussion of the challenges in terms of accuracy, processing time, cost, etc. is provided. The main purpose of this review is to provide a comprehensive overview of public available stereo visual SLAM frameworks and their corresponding pros and cons in different real-world scenarios.