Li, X.; Liu, B.; Lv, S.; Li, M.; Liu, C. A Fast Registration Method for MEMS LiDAR Point Cloud Based on Self-Adaptive Segmentation. Electronics 2023, 12, 4006. https://doi.org/10.3390/electronics12194006
Li, X.; Liu, B.; Lv, S.; Li, M.; Liu, C. A Fast Registration Method for MEMS LiDAR Point Cloud Based on Self-Adaptive Segmentation. Electronics 2023, 12, 4006. https://doi.org/10.3390/electronics12194006
Li, X.; Liu, B.; Lv, S.; Li, M.; Liu, C. A Fast Registration Method for MEMS LiDAR Point Cloud Based on Self-Adaptive Segmentation. Electronics 2023, 12, 4006. https://doi.org/10.3390/electronics12194006
Li, X.; Liu, B.; Lv, S.; Li, M.; Liu, C. A Fast Registration Method for MEMS LiDAR Point Cloud Based on Self-Adaptive Segmentation. Electronics 2023, 12, 4006. https://doi.org/10.3390/electronics12194006
Abstract
Abstract: The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved NDT (Normal Distribution Transform) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the ISS (Intrinsic Shape Signature) algorithm is utilized to analyze the point cloud features and extract keypoints, the 4PCS (4-Point Congruent Sets) algorithm is then employed to calculate the initial pose under the constraints of the keypoint set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using KD-tree to obtain the density features of keypoints, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected data sets, the results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference.
Keywords
Intelligent information perception; point cloud registration; vehicle mounted MEMS LiDAR; SSM-NDT algorithm; self-adaptive segmentation model; autonomous vehicle
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
