Performance of localization based on visual slam and lidar point cloud registration

Abstract. Self-driving car technology has become increasingly popular in recent years. Traditionally, these cars rely on GNSS and INS, but limitations in urban environments can affect their effectiveness. GNSS technology has developed to address this issue, but it still has limitations in certain environments. LiDAR technology has emerged as a solution to obtain high-precision point cloud maps and SLAM technology is now used to integrate these maps with sensor data, such as cameras, to achieve precise positioning. This enables visual SLAM to be performed in environments where GNSS signals are blocked. In this research, we aim to use SLAM technology to obtain posterior environmental information and match it with prior high-precision point cloud maps. The research will start with hardware configuration, actual measurement and analysis of SLAM algorithms, and 3D point cloud matching methods. Results and benefits will be analysed to compare the advantages and disadvantages of point cloud matching algorithms and applicable hardware.