多视图对象姿势分布跟踪用于移动机器人的预抓计划
Lakshadeep Naik1, Thorbjørn Mosekjær Iversen1, Jakob Wilm1
1SDU Robotics, Mærsk Mc-Kinney Møller Institute (MMMI), Faculty of Engineering, University of Southern Denmark, Odense, Denmark.
Frontiers in robotics and AI
|January 30, 2026
概括
这项研究引入了一个多视图粒子过器来跟踪移动操纵器对象姿势分布. 它通过融合摄像头视图来改善掌握计划和机器人操纵的稳定性,克服单个摄像头的局限性.
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:
- 精确的6D对象姿势跟踪对于移动操纵任务,如掌握计划至关重要.
- 接近移动操纵器的有限摄像头视图对精确的姿势估计构成重大挑战.
- 现有的单视图跟踪方法在阻塞和不完全的物体可见性方面扎.
研究的目的:
- 开发一个强大的框架来跟踪在移动操纵器方法中对象的6D姿势分布.
- 通过提供准确的姿势信息来提高掌握计划的效率和可靠性.
- 通过合并多视图观测来克服单摄像头视图的局限性.
主要方法:
- 提出了一个基于颗粒过器的多视图框架,扩展PoseRBPF.
- 从移动操纵器的摄像头和外部固定摄像头的观察结果的融合.
- 建模对象的姿势后面作为一个多模式分布.
- 引入了数据融合,重新采样和从跟踪分布中估计位的技术.
主要成果:
- 与以前的方法相比,拟议的框架可以更准确地量化对象的位置和相关的不确定性.
- 有效处理来自多个摄像头的杂和相互矛盾的观测.
- 对于移动机器人来说,在抓取前规划方面取得了明显的改进.
结论:
- 多视图方法显著提高了6D对象姿势分布跟踪的准确性和稳定性.
- 外部摄像头数据的融合对于在操纵器接近时补偿有限的视图至关重要.
- 该框架为现实世界移动操纵应用提供了实用实用性.
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