Pandora: a dynamic object removal method for LiDAR SLAM based on polar adaptive binary-encoded height comparison

Abstract In the domain of autonomous driving, 3D point cloud maps generated by light detection and ranging simultaneous localization and mapping are crucial for navigation, as they provide essential spatial constraints by leveraging static environmental features. However, residual traces of dynamic objects within these maps compromise their reliability. Existing methods for dynamic object removal often misclassify static points while eliminating dynamic ones, failing to achieve an optimal balance between preservation and deletion. To address this challenge, we propose Pandora, a novel approach that combines polar grids with adaptive binary height encoding to accurately distinguish dynamic objects while preserving high-quality static structures across diverse environments. The method employs an adaptive hierarchical strategy for point cloud localization and a bitwise matrix comparison mechanism to detect dynamic regions. Additionally, it identifies near-ground dynamic points through density analysis and incorporates a dual ray-casting module to mitigate misclassification of static points caused by occlusions. By leveraging parallel processing during per-frame comparisons, the computational overhead associated with coordinate transformations is significantly reduced. Experimental evaluation on the KITTI and semi-indoor datasets demonstrates that Pandora achieves superior overall performance, with dynamic and static point identification accuracy reaching 98.6%, while maintaining high processing efficiency.