Localization of Intelligent Vehicles Based on LiDAR: A Review

The recent research on location approaches of the intelligent vehicle based on Light Detection and Ranging (LiDAR) is analyzed in this paper. According to the features of these approaches, it can be divided into three categories: simultaneous localization and mapping (SLAM), offline mapping and online localization (OMOL) and fusion localization (FL). Past research and applications of the main algorithms and critical research scenarios in each localization approaches are reviewed. Three aspects of the current trend in location approaches of the intelligent vehicle based on LiDAR are discussed. Based on object detection, object recognition and object analysis algorithms in the field of deep learning, semantic SLAM and real-time three-dimensional reconstruction are important research trends for SLAM. The performance of robustness and real-time performance of localization algorithm of intelligent vehicles based on LiDAR need to be improved. As a sensing sensor, multi-line LiDAR will return a large amount of environmental information, and the matching method for all LiDAR points cannot meet the real-time requirements of practical applications. Extracting appropriate features from environmental information as the subject for matching is the key to improving the real-time performance of the algorithm. At present, most researches on vehicle localization are based on the movement data of the vehicle close to the smooth road surface, but it cannot handle the localization problem in complex scenes such as a road with great slope degree and special weather conditions. It is mentioned that localization of intelligent vehicles based on multi-sensors, like Global Navigation Satellite System (GNSS) and inertial navigation system (INS), in complex surroundings is an inevitable trend.