Online Mapping from Weight Matching Odometry and Highly Dynamic Point Cloud Filtering via Pseudo-Occupancy Grid

Efficient locomotion in autonomous driving and robotics requires clearer visualization and more precise map. This paper presents a high accuracy online mapping including weight matching LiDAR-IMU-GNSS odometry and an object-level highly dynamic point cloud filtering method based on a pseudo-occupancy grid. The odometry integrates IMU pre-integration, ground point segmentation through progressive morphological filtering (PMF), motion compensation, and weight feature point matching. Weight feature point matching enhances alignment accuracy by combining geometric and reflectance intensity similarities. By computing the pseudo-occupancy ratio between the current frame and prior local submaps, the grid probability values are updated to identify the distribution of dynamic grids. Object-level point cloud cluster segmentation is obtained using the curved voxel clustering method, eventually leading to filtering out the object-level highly dynamic point clouds during the online mapping process. Compared to the LIO-SAM and FAST-LIO2 frameworks, the proposed odometry demonstrates superior accuracy in the KITTI, UrbanLoco, and Newer College (NCD) datasets. Meantime, the proposed highly dynamic point cloud filtering algorithm exhibits better detection precision than the performance of Removert and ERASOR. Furthermore, the high-accuracy online mapping is built from a real-time dataset with the comprehensive filtering of driving vehicles, cyclists, and pedestrians. This research contributes to the field of high-accuracy online mapping, especially in filtering highly dynamic objects in an advanced way.