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使用概率密度函数进行对象意识的语义映射,用于室内重新定位和路径规划
Alicia Mora1, Alberto Mendez2, Luis Moreno2
1RoboticsLab, Department of Automation and Systems Engineering, Universidad Carlos III de Madrid, Leganes, Madrid, 28911, Spain. almorav@ing.uc3m.es.
Scientific reports
|February 17, 2026
概括
本研究介绍了室内机器人对象感知语义映射框架. 它使用概率密度函数 (PDF) 创建紧,强大的3D地图,改善复杂环境中的导航和重新定位.
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:
- 室内机器人需要适应复杂环境的可扩展的语义图.
- 现有的语义地图要么过于密集,计算成本昂贵,要么缺乏几何细节.
- 在地图细节和计算效率之间存在一种权衡,限制了现实世界的应用.
研究的目的:
- 为室内机器人开发一种新的对象感知语义映射框架.
- 创建一个紧的,强大的,可扩展的语义表示,平衡细节和效率.
- 为了提高机器人的能力在搬迁,路径规划和场景理解.
主要方法:
- 使用概率密度函数 (PDF) 建模关键静态对象.
- 通过3D点云处理检测对象并将其编码为2D概率占用分布.
- 利用差异进化和库尔巴克 - 莱布勒分歧,在没有先前设置的情况下进行强大的迁移.
主要成果:
- 拟议的框架提供了一个紧而强大的表示,保留语义身份和几何形状.
- 它有效地处理噪音和部分视图,使全球重新定位和语义信息的路线规划成为可能.
- 在模糊或杂乱的场景中,表现出比传统方法更好的性能.
结论:
- 面向对象的概率绘制框架为多种机器人行为提供了统一的表示.
- 这种方法支持用于上下文感知导航的功能场景理解.
- 在基准数据集和现实世界公寓环境上进行验证,显示了显著的优势.
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