Fully Automatic Large-Scale Point Cloud Mapping for Low-Speed Self-Driving Vehicles in Unstructured Environments

Abstract

This paper presents a fully automatic large-scale point cloud mapping system for low-speed self-driving vehicles and robots operating in complicated unstructured environments. The proposed system robustly fuses multiple sensor inputs from IMU, RTK, wheel speed encoder, and LiDAR point clouds into a factor graph to obtain a globally consistent point cloud map. A robust two-stage optimization routine is proposed to tackle the practical issues that arise from real-world environments, such as handling unstable RTK signals, LiDAR degeneracy in structure-less areas, and cooperative mapping tasks. The system has been widely used for over 500 vehicles and 1,000 maps since 2019. We present a comparative evaluation with popular mapping algorithms in terms of accuracy and robustness to various challenging scenes.