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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.08937 |
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| _version_ | 1866913106825838592 |
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| author | Kim, Daebeom Lee, Seungjae Jang, Seoyeon Marsim, Kevin Christiansen Myung, Hyun |
| author_facet | Kim, Daebeom Lee, Seungjae Jang, Seoyeon Marsim, Kevin Christiansen Myung, Hyun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08937 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Raymoval: Raycasting-based Dynamic Object Removal for Static 3D Mapping Kim, Daebeom Lee, Seungjae Jang, Seoyeon Marsim, Kevin Christiansen Myung, Hyun Robotics 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. |
| title | Raymoval: Raycasting-based Dynamic Object Removal for Static 3D Mapping |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.08937 |