Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation

Dynamic objects are an inherent part of our world, but their presence deteriorates the performance of various localization, navigation, and SLAM algorithms. This not only makes it important but necessary to remove these dynamic points from the map before they can be used for other tasks. In this paper, we address the problem of building maps of the static aspects of the world by detecting and removing dynamic points from the source point clouds. We target a map cleaning approach that removes the dynamic points and maintains a high quality of the generated static map. To this end, we propose a novel ground segmentation method and integrate it into the OctoMap to better distinguish between the moving objects and static road backgrounds. We evaluate our approach using SemanticKITTI for both dynamic object removal and ground segmentation algorithms. The evaluation results show that our method outperforms the baseline methods in both tasks and achieves good performance in generating clean maps.