Research on NDT-based Positioning for Autonomous Driving

Abstract

Autonomous driving technology is one of the currently popular technologies, while positioning is the basic problem of autonomous navigation of autonomous vehicles. GPS is widely used as a relatively mature solution in the outdoor open road environment. However, GPS signals will be greatly affected in a complex environment with obstruction and electromagnetic interference, even signal loss may occur if serious, which has a great impact on the accuracy, stability and reliability of positioning. For the time being, L4 and most L3 autonomous driving modules still provide registration and positioning based on the high-precision map constructed. Based on this, this paper elaborates on the reconstruction of the experimental scene environment, using the SLAM (simultaneous localization and mapping) method to construct a highprecision point cloud map. On the constructed prior map, the 3D laser point cloud NDT matching method is used for real-time positioning, which is tested and verified on the “JAC Electric Vehicle” platform. The experimental results show that this algorithm has high positioning accuracy and its real-time performance meets the requirements, which can replace GPS signals to complete the positioning of autonomous vehicles when there is no GPS signal or the GPS signal is weak, and provide positioning accuracy meeting the requirements.

Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

“…Although mobile LiDAR technology has been applied in underground spatial information measurement, there are still challenges for large-scale 3D data acquisition in long tunnels and indoor spaces without GNSS signals due to the lack of significant features or complex environments. The current research on the rapid acquisition and mapping of underground space information can be divided into four categories: mobile measurement system for measuring and its improvement [11][12][13][14][15][16][17][18][19][20][21], laser based on simultaneous localization and mapping (SLAM) algorithm [22][23][24][25], monocular/ binocular vision sensor [26], and RGBD (Red, Green, Blue, Depth map) vision sensor mobile robot system [27][28][29][30] and its corresponding unmanned aerial vehicle (UAV) [31][32][33][34][35] platform.…”