Raymoval: Raycasting-based Dynamic Object Removal for Static 3D Mapping
For robotics researchers needing reliable static maps for navigation, this method offers a practical solution to dynamic object removal, though it is an incremental improvement over existing techniques.
The paper proposes a raycasting-based module for removing dynamic objects from 3D maps, achieving consistent static mapping with reduced residual dynamics and over-removal on SemanticKITTI and a custom dataset.
Static mapping is fundamental to robot navigation, providing a persistent geometric prior and a consistent reference for long-term autonomy. However, dynamic objects leave residual traces and cause surface loss, which reduces map consistency. We propose a raycasting-based module for dynamic object removal in static 3D mapping. Each scan is projected onto an azimuth-elevation grid, and for every viewing direction we compare the bin-wise minimum range with the map's first-hit distance computed by raycasting. Furthermore, we apply a raycast consistency test that separates dynamic from static points. Finally, a spatial consistency validation step refines labels, producing static maps with lower residual dynamics and reduced over-removal. We evaluate our approach quantitatively and qualitatively on SemanticKITTI and a challenging custom dataset, and show consistent static mapping results.