A key challenge for autonomous vehicles is to navigate in unseen dynamic environments. Separating moving objects from static ones is essential for navigation, pose estimation, and understanding how other traffic participants are likely to move in the near future. In this work, we tackle the problem of distinguishing 3D LiDAR points that belong to currently moving objects, like walking pedestrians or driving cars, from points that are obtained from non-moving objects, like walls but also parked cars. Our approach takes a sequence of observed LiDAR scans and turns them into a voxelized sparse 4D point cloud. We apply computationally efficient sparse 4D convolutions to jointly extract spatial and temporal features and predict moving object confidence scores for all points in the sequence. We develop a receding horizon strategy that allows us to predict moving objects online and to refine predictions on the go based on new observations. We use a binary Bayes filter to recursively integrate new predictions of a scan resulting in more robust estimation. We evaluate our approach on the SemanticKITTI moving object segmentation challenge and show more accurate predictions than existing methods. Since our approach only operates on the geometric information of point clouds over time, it generalizes well to new, unseen environments, which we evaluate on the Apollo dataset. Index Terms-Semantic Scene Understanding; Deep Learning Methods I. INTRODUCTION D ISTINGUISHING moving from static objects in 3D LiDAR data is a crucial task for autonomous systems and required for planning collision-free trajectories and navigating safely in dynamic environments. Moving object segmentation (MOS) can improve localization [5], [7], planning [34], mapping [5], scene flow estimation [2], [15], [37], or the prediction of future states [38], [40]. There are mapping approaches that identify if observed points are potentially moving or have moved throughout the mapping process [1], [7], [16], [28]. On the contrary, identifying objects that are actually moving within a short time horizon are of interest for online navigation [34], can improve scene flow estimation between two consecutive point clouds [2], [15], [37], or support predicting a future state of the environment [40].
