在动态环境下,基于相互信息的层次 NBV 决策,用于动态语义视觉 SLAM.
Zhenyuan Yang1, Ash Wan Yaw Sang1, M A Viraj J Muthugala2
1Engineering Product Development, Singapore University of Technology and Design, Singapore, 487372, Singapore.
Scientific reports
|January 20, 2026
概括
本研究介绍了一种强大的主动同步定位和映射 (A-SLAM) 方法,使用相互信息来导航动态环境. 该方法通过排除移动的物体和优化路径规划来提高机器人映射和跟踪精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 同时定位和映射 (SLAM) 算法在动态环境中扎,因为移动的物体会影响准确性.
- 现有的语义SLAM方法可能会在活动SLAM中失去跟踪的风险,当排除没有强大的路径规划的特征时.
研究的目的:
- 开发基于实时,相互信息的积极SLAM (A-SLAM) 方法,以在动态环境中提高稳定性.
- 为了改善路线规划和保持跟踪精度,尽管环境变化.
主要方法:
- 提出了一种新的A-SLAM算法,利用相互信息进行实时处理.
- 整合了两个下一个最佳视图 (NBV) 决策模块,以优化路径规划.
- 在映射过程中实现了动态对象排除.
主要成果:
- 拟议的A-SLAM方法在模拟和现实世界的动态环境中表现出增强的稳定性和效率.
- 成功排除动态对象,同时保持本地化和映射精度.
- 验证了算法在实际应用中的部署能力.
结论:
- 开发的基于相互信息的A-SLAM方法有效地应对动态环境的挑战.
- 整合NBV模块显著改善了路径规划和稳定性.
- 该算法为复杂,不断变化的条件下机器人导航和绘图提供了可靠的解决方案.
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