Static map generation from 3D LiDAR point clouds exploiting ground segmentation

A clean and reliable map of the environment is key for a variety of robotic tasks including localization, path planning, and navigation. Dynamic objects are an inherent part of our world, but their presence often deteriorates the performance of various mapping 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 such as path planning. 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 map of the static part of the world. To this end, we propose a novel offline 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 as well as on the Apollo dataset. The evaluation results show that our method outperforms the baseline methods in both tasks and achieves good performance in generating clean maps over different datasets without any change in the parameters.