神经辐射场动态场景SLAM基于光线分割和捆绑调整
Yuquan Zhang1,2, Guosheng Feng1,3
1School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了一种用于动态场景的新型神经隐性SLAM方法. 它提高了3D重建和跟踪准确性,在具有挑战性的环境,随着变化的照明和运动.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 三维重建的3D重建
背景情况:
- 神经隐性SLAM在静态场景中表现出色,但在动态环境和照明变化中扎.
- 处理现实世界的场景与运动和照明变化是SLAM的一个关键挑战.
研究的目的:
- 开发一种神经隐性SLAM方法,能够稳定地处理动态场景.
- 在具有挑战性的现实世界场景中提高3D重建的准确性和质量.
主要方法:
- 使用卢卡斯-卡纳德 (LK) 光流的关键选择和跟踪切换方法.
- 一个基于语义的动态和静态像素的联合估计,使用条件随机场 (CRF).
- 开发用于动态场景建模的受约束损失函数.
主要成果:
- 拟议的方法在动态场景数据集 (TUM RGB-D,Openloris,波恩) 上显示出卓越的性能.
- 与现有的神经隐性SLAM系统相比,重建质量的显著改善.
- 在充满挑战的动态环境中提高追踪精度.
结论:
- 新型的神经隐性SLAM方法有效地解决了动态场景重建的局限性.
- 该方法为现实世界SLAM应用程序提供了强大的解决方案,具有运动和照明可变性.
- 这项工作推进了用于复杂环境的神经隐性SLAM的最新技术.
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