Raymoval: Raycasting-based Dynamic Object Removal for Static 3D Mapping

📅 2026-05-09
📈 Citations: 0
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🤖 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.
Problem

Research questions and friction points this paper is trying to address.

static 3D mapping
dynamic object removal
map consistency
residual traces
surface loss
Innovation

Methods, ideas, or system contributions that make the work stand out.

raycasting
dynamic object removal
static 3D mapping
spatial consistency
lidar processing
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