Dynamic Obstacle Point Cloud Detection and Removal Algorithm Based on Dual-map Collaboration

To address the issues of point cloud feature uncertainty, registration failure, and map distortion in laser-based Simultaneous localization and mapping (SLAM) caused by dynamic disturbances such as moving obstacles, hollow-structured objects, and equipment tilt in complex indoor scenes, a dynamic obstacle point cloud detection and removal algorithm based on dual-map collaboration was proposed. First, a multi-resolution grid map was constructed to optimize the registration efficiency and accuracy of the correlative scan matching (CSM) algorithm. On this basis, a dual-map system consisting of a static reference map and a local dynamic obstacle map was established, enabling accurate identification and efficient removal of dynamic and spurious point clouds through a bidirectional constraint mechanism. The experimental results from both simulation and real-world environments show that the proposed algorithm stabilizes the CSM registration score above 0.85, effectively avoiding registration failure and map distortion. The dynamic obstacle recognition rate reaches 98.75%, and the absolute translation error (ATE) is optimized by 3.68% on average. Compared with conventional grid-probability updating strategies, the proposed algorithm significantly improves the overall performance of laser SLAM systems in terms of registration stability, dynamic environmental perception, and global localization accuracy. A distortion-free and highly reliable static grid map can be constructed, providing an effective solution for autonomous localization and mapping in indoor dynamic scenes.