OTD: An Online Dynamic Traces Removal Method Based on Observation Time Difference

Three-dimensional point cloud map plays an important role in 3-D reconstruction, autonomous robot navigation, autonomous driving, and environmental monitoring. Nowadays, 3-D point cloud map could be obtained through frame-by-frame accumulation of LiDAR point cloud using SLAM technology. However, during this process, the movements of dynamic objects in the environment will leave a large number of traces on the point cloud map, causing difficulties in the subsequent use of the map, such as city model construction and robot autonomous navigation. Therefore, dynamic traces removal is crucial for building clean static maps. However, existing methods for dynamic traces removal are mostly offline, which inevitably incurs additional time consumption. To address this problem, this article proposes an online dynamic traces removal method. We take voxels as the smallest unit for dynamic traces removal, and voxels containing dynamic traces are called dynamic voxels, otherwise they are called static voxels. Our method is based on the assumption that static voxels always appear and disappear simultaneously with the ground below them. Therefore, we call voxel that appears later than the ground as suddenly appear dynamic voxel, and voxel that disappears earlier than the ground as suddenly disappear dynamic voxel. We call this method of judging dynamic voxels as observation time difference, and propose downward retrieval and upward retrieval methods to remove these two types of dynamic voxels, respectively. We tested our proposed method on SemanticKITTI, UrbanLoco, and author-collected datasets. Experimental results show that our method is more accurate and robust than existing online dynamic traces removal methods. And compared with other methods, our method shortens the time of processing each frame of point cloud by more than 60%. Our method is open-sourced on GitHub:
https://github.com/RongguangWu/OTD
.