3D-CSTM: A 3D continuous spatio-temporal mapping method

Abstract

3D mapping is now essential for urban infrastructure resource monitoring, autonomous driving, and the fulfillment of digital earth. LiDAR-based Simultaneous Localization And Mapping (SLAM) technology has been widely studied because of its efficient application in the reconstruction of the 3D environments that benefitted from its sensor characteristic. However, the sparsity of low cost LiDAR (Light Detection And Ranging) sensor provides great challenge for it. Insufficient or poor distribution of the constraints as well as the motion distortion in one scan of point cloud would deteriorate the pose estimation in SLAM. In this paper, a novel 3D mapping method based only on the LiDAR data is proposed to enhance the performance of SLAM-based 3D point clouds map reconstruction. The proposed system consists of two main modules: localization module for rapid ego-motion estimation within the surrounding environment, and mapping module for accurate point clouds fusion. As for the localization, scan-to-map point cloud registration scheme are adopted where structural features are firstly extracted to provide valid correspondences. What’s more, an effective method is figured out to balance the cost factors for the optimization function of pose estimation in the process. This further handles the degenerated cases for cloud registration. Focusing on improving the quality of the map, a spline motion model is integrated with non-rigid point cloud fusion to facilitate the continuous and nonrepetitive mapping of the environment. Extensive experiments are carried out for the verification of our system including large-scale outdoor and long-corridor scenes. According to the experimental results, our system has achieved 0.78‰closure error and 27.5% improvement in the APE (Absolute Pose Errors), outperforming the state-of-the-art LiDAR-based SLAM (e.g. LeGO-LOAM) frameworks in our test sites. To specifically verify the performance of 3D mapping, the precision of 3D points is quantified through comparing with the high-precision point cloud maps collected by MLS (Mobile Laser Scanning) and TLS (Terrestrial Laser Scanning). The RMS of point-to-plane distances have improved 20% based on the fitted Weibull distribution. In addition, some ablation tests are conducted to reveal the efficacy of different components in our system.