通过表面标记全局不变性实现高精度6自由度触觉位姿追踪
InvariantCloud: A Globally Invariant, Uniquely Indexed Point Cloud Framework for Robust 6-DoF Tactile Pose Tracking
- 利用视觉触觉传感器表面标记的全局不变性进行点云注册
- 在长序列操作中实现更优的偏航角追踪精度与重定位重复性
- 适合需要高精度触觉感知的机器人抓取与长期操作任务
模仿学习与视觉-语言模型的进展凸显了高保真触觉感知的重要性，6自由度触觉物体位姿估计是精确机器人操作的关键基础。我们提出InvariantCloud，一种基于视觉触觉传感器表面标记星座全局不变性的6-DoF位姿估计框架。相比现有方法，该框架采用一次式全局不变点云配准，有效抑制累积漂移，并克服了长期以来对偏航（Z轴）旋转估计不准确的难题。实验验证表明，InvariantCloud在长序列操作任务中相较于现有基准展现出更优的偏航跟踪精度与重定位重复性，证明了其在精度与鲁棒性方面的优势。
原文摘要 · Abstract (English)
Recent advances in imitation learning and vision-language models highlight the need for high-fidelity tactile perception, with 6-DoF tactile object pose estimation providing a crucial foundation for precise robotic manipulation. We introduce InvariantCloud, a 6-DoF pose estimation framework that leverages the global invariance of surface marker constellations on vision-based tactile sensors. In contrast to recent approaches, our one-shot globally invariant point cloud registration suppresses cumulative drift and overcomes long-standing limitations in accurately estimating yaw (Z-axis) rotation. Experimental verifications show that InvariantCloud achieves superior yaw tracking accuracy and re-localization repeatability compared to existing benchmarks, demonstrating its precision and robustness in long-sequence manipulation tasks.
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