语义和信托辅助图形同时定位和映射 (SF-GraphSLAM) 用于机器人在太空中组装和维护大型结构结构
Samantha Chapin1, William Chapin1, Erik Komendera1
1Field and Space Experimental Robotics (FASER) Laboratory, Mechanical Engineering Department, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Frontiers in robotics and AI
|November 17, 2025
概括
本研究介绍了一种新的语义和信任辅助图形同时定位和映射 (SF-GraphSLAM) 算法,用于机器人在空间组装. 这种方法通过使用视觉标签和语义信息来提高建造大型托架结构的准确性.
科学领域:
- 机器人和太空工程 机器人和太空工程
- 计算机视觉和同时定位和映射 (SLAM)
背景情况:
- 机器人在太空中组装大型结构结构需要精确的定位和映射.
- 目前的SLAM方法缺乏针对空间建筑的独特挑战的特异性.
研究的目的:
- 开发一个专门的SF-GraphSLAM算法用于机器人在太空中的装配和维修.
- 为了提高建造大型空间结构的准确性和效率.
主要方法:
- 通过将模块与AprilTag视觉标记器相关联,减少了模块的观察.
- 集成的语义信息关于所需的模块转换成一个因子图模型.
- 使用基于摄像头的机器人模型模拟了算法.
主要成果:
- 与没有结构知识的最先进方法相比,SF-GraphSLAM算法表现出更好的性能.
- 通过使用AprilTags和语义数据,成功地减少了观察到的元素数量.
- 允许在组装过程中创建托架结构的虚拟双胞胎.
结论:
- 拟议的SF-GraphSLAM方法是SLAM在空间组装中的重大进步.
- 准确的虚拟双胞胎生成对于确保大型空间结构满足设计要求至关重要.
- 这项研究解决了SLAM欠发达领域对空间建筑的关键需求.
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