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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.09962 |
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| _version_ | 1866910077323051008 |
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| author | He, Yicheng Yu, Jingwen Chen, Guangcheng Zhang, Hong |
| author_facet | He, Yicheng Yu, Jingwen Chen, Guangcheng Zhang, Hong |
| contents | Maintaining an up-to-date map that accurately reflects recent changes in the environment is crucial, especially for robots that repeatedly traverse the same space. Failing to promptly update the changed regions can degrade map quality, resulting in poor localization, inefficient operations, and even lost robots. 3D Gaussian Splatting (3DGS) has recently seen widespread adoption in online map reconstruction due to its dense, differentiable, and photorealistic properties, yet accurately and efficiently updating the regions of change remains a challenge. In this paper, we propose VG-Mapping, a novel online 3DGS-based mapping system tailored for such semi-static scenes. Our approach introduces a variation-aware density control strategy that decouples Gaussian density regulation from optimization. Specifically, we identify regions with variation to guide initialization and pruning, which avoids the use of stale information in defining the starting point for the subsequent optimization. Furthermore, to address the absence of public benchmarks for this task, we construct a RGB-D dataset comprising both synthetic and real-world semi-static environments. Experimental results demonstrate that our method substantially improves the rendering quality and map update efficiency in semi-static scenes. The code and dataset are available at https://github.com/heyicheng-never/VG-Mapping. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VG-Mapping: Variation-aware Density Control for Online 3D Gaussian Mapping in Semi-static Scenes He, Yicheng Yu, Jingwen Chen, Guangcheng Zhang, Hong Robotics Maintaining an up-to-date map that accurately reflects recent changes in the environment is crucial, especially for robots that repeatedly traverse the same space. Failing to promptly update the changed regions can degrade map quality, resulting in poor localization, inefficient operations, and even lost robots. 3D Gaussian Splatting (3DGS) has recently seen widespread adoption in online map reconstruction due to its dense, differentiable, and photorealistic properties, yet accurately and efficiently updating the regions of change remains a challenge. In this paper, we propose VG-Mapping, a novel online 3DGS-based mapping system tailored for such semi-static scenes. Our approach introduces a variation-aware density control strategy that decouples Gaussian density regulation from optimization. Specifically, we identify regions with variation to guide initialization and pruning, which avoids the use of stale information in defining the starting point for the subsequent optimization. Furthermore, to address the absence of public benchmarks for this task, we construct a RGB-D dataset comprising both synthetic and real-world semi-static environments. Experimental results demonstrate that our method substantially improves the rendering quality and map update efficiency in semi-static scenes. The code and dataset are available at https://github.com/heyicheng-never/VG-Mapping. |
| title | VG-Mapping: Variation-aware Density Control for Online 3D Gaussian Mapping in Semi-static Scenes |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.09962 |