  Identifying moving objects is an essential capability for autonomous systems,
as it provides critical information for pose estimation, navigation, collision
avoidance, and static map construction. In this paper, we present MotionBEV, a
fast and accurate framework for LiDAR moving object segmentation, which
segments moving objects with appearance and motion features in the bird's eye
view (BEV) domain. Our approach converts 3D LiDAR scans into a 2D polar BEV
representation to improve computational efficiency. Specifically, we learn
appearance features with a simplified PointNet and compute motion features
through the height differences of consecutive frames of point clouds projected
onto vertical columns in the polar BEV coordinate system. We employ a
dual-branch network bridged by the Appearance-Motion Co-attention Module (AMCM)
to adaptively fuse the spatio-temporal information from appearance and motion
features. Our approach achieves state-of-the-art performance on the
SemanticKITTI-MOS benchmark. Furthermore, to demonstrate the practical
effectiveness of our method, we provide a LiDAR-MOS dataset recorded by a
solid-state LiDAR, which features non-repetitive scanning patterns and a small
field of view.
