🤖 AI Summary
Dynamic objects in static 3D mapping often introduce residual artifacts and surface incompleteness, compromising map consistency. To address this issue, this work proposes a ray-casting-based method for dynamic point removal: laser scans are projected onto an azimuth-elevation grid, and dynamic points are accurately identified and eliminated through first-hit distance comparison, ray consistency verification, and spatial consistency optimization. The approach innovatively integrates geometric and spatial contextual information, effectively suppressing dynamic residuals while avoiding over-removal of static structures. Experimental results demonstrate that the proposed method significantly enhances the completeness and consistency of static 3D maps on both the SemanticKITTI benchmark and a challenging self-collected dataset.
📝 Abstract
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.