ScanTrimmer: an Online Dynamic Objects Removal Framework in Laser Scan for Robust Localization.

The Simultaneous Localization and Mapping (SLAM) technique is often employed in robotic localization tasks. For lidar-based SLAM, point cloud registration (PCR) is one of the crucial factors for overall localization precision. However, the quality of point-to-point correspondence between two distinct point clouds obtained by PCR is environment dependent. Particularly in dynamic environments, the movement of objects can disrupt this correspondence, resulting in a degradation of localization accuracy. Therefore, previous SLAM methods often fail in environments with ample dynamic objects. Intuitively, we can remove dynamic points from the input laser scan to degenerate the influence of environment dynamicity. However, identifying dynamic objects precisely from point clouds which is challenging for the SLAM running in robotic systems with high real-time requirements. This paper proposes a new online lidar-based SLAM framework, i.e., ScanTrimmer, which significantly enhances the localization accuracy of the SLAM systems by detecting and deleting potential dynamic points through space-based comparison in point cloud distribution between scans taken at a specific interval. The experiments demonstrate that ScanTrimmer can improve the localization accuracy by 44.69% on the simulated dataset and 25.99% on the real-world dataset compared with the previous method.