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BDGS-SLAM:一个概率的3D高斯分片框架,用于在动态环境中的强大的SLAM
Tianyu Yang1, Shuangfeng Wei1,2,3, Jingxuan Nan1
1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究介绍了贝叶斯动态高斯分片SLAM (BDGS-SLAM),用于在动态环境中实现强大的机器人导航. 这种新的框架提高了映射精度,并通过智能处理移动对象来减少染工件.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 同时定位和映射 (SLAM) 对于机器人导航和增强现实至关重要.
- 3D Gaussian Splatting (3DGS) 为SLAM提供实时,高保真染,但在动态对象方面存在困难.
- 现有的3DGS-SLAM方法在动态环境中面临映射错误和跟踪漂移.
研究的目的:
- 为动态环境提出BDGS-SLAM,一个贝叶斯动态高斯分裂SLAM框架.
- 为了提高SLAM在移动物体存在时的准确性和稳定性.
- 为了提高场景重建的保真度,并在动态场景中减少染文物.
主要方法:
- 整合YOLOv5语义检测与贝叶斯选,创建一个动态先前概率模型来识别动态高斯.
- 采用多视图概率更新机制,具有指数衰变因子,以恢复错误除的静态高斯.
- 实施适应性动态高斯优化策略,以减轻动态元素对染的影响,并保持场景完整性.
主要成果:
- 在动态环境中,BDGS-SLAM实现了与基线方法相比较的跟踪精度.
- 与现有的3DGS-SLAM方法相比,提出的方法产生较少的染工件.
- BDGS-SLAM展示了高保真场景重建,有效地处理动态对象.
结论:
- 在动态环境中,BDGS-SLAM为SLAM提供了强大的解决方案.
- 该框架成功地解决了传统3DGS-SLAM在处理移动物体方面的局限性.
- BDGS-SLAM为现实应用提供了更好的场景重建质量和跟踪稳定性.
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