In recent years, with the rapid development of autonomous driving technologies, the demand of intelligent vehicles for environment perception technology is also higher and higher. Due to the high accuracy of LiDAR data that can better obtain the 3D information in the environment, it has become a research hotspot in the field of 3D target detection. In order to provide more accurate environmental information for intelligent vehicles, the main research contents in the field of 3D target detection by LiDAR are summarized. Firstly, the advantages and disadvantages of various environment sensing sensors for self-driving vehicles are analyzed; secondly, according to the different data processing methods in 3D target detection algorithms, the detection algorithms based on point cloud and the detection algorithms fused with image and point cloud are reviewed; then, the mainstream self-driving datasets and their evaluation methods for 3D target detection are sorted out; and finally, the current 3D target detection algorithms for point cloud are summarized and outlooked. The results show the importance of the 2D view method and the multimodal fusion method in the current research for the development of autonomous driving technologies.

Machine vision  /  LiDAR  /  Autonomous driving  /  3D object detection  /  Radar point cloud