探索SE(3)流形上的类别级铰接物体位姿跟踪
Exploring Category-level Articulated Object Pose Tracking on SE(3) Manifolds
AI总结：
针对铰接物体因运动学约束导致位姿跟踪研究不足的问题，提出SE(3)流形上基于点对特征（PPF）的PPF-Tracker框架，通过准规范化、位姿投票与语义约束提升跟踪效果，推动相关领域发展。
摘要：
铰接物体在日常生活和机器人操作任务中十分常见，但与刚性物体相比，由于其固有的运动学约束，铰接物体的位姿跟踪仍是一个研究不足的问题。为应对这些挑战，本研究提出一种新颖的基于点对的位姿跟踪框架，称为PPF-Tracker。该框架首先在SE(3)李群空间中对点云进行准规范化，然后利用SE(3)的不变性，通过点对特征（PPF）对铰接物体建模，以预测位姿投票参数。最后，融入关节轴的语义信息，为铰接物体的所有部件施加统一的运动学约束。PPF-Tracker在合成数据集和真实场景中均进行了系统评估，证明其在多样且具有挑战性的环境中具有较强的泛化能力。实验结果凸显了PPF-Tracker在铰接物体多帧位姿跟踪中的有效性和鲁棒性。我们相信这项工作能够推动机器人学、具身智能和增强现实领域的发展。代码可在https://github.com/mengxh20/PPFTracker获取。
英文摘要：
Articulated objects are prevalent in daily life and robotic manipulation tasks. However, compared to rigid objects, pose tracking for articulated objects remains an underexplored problem due to their inherent kinematic constraints. To address these challenges, this work proposes a novel point-pair-based pose tracking framework, termed \textbf{PPF-Tracker}. The proposed framework first performs quasi-canonicalization of point clouds in the SE(3) Lie group space, and then models articulated objects using Point Pair Features (PPF) to predict pose voting parameters by leveraging the invariance properties of SE(3). Finally, semantic information of joint axes is incorporated to impose unified kinematic constraints across all parts of the articulated object. PPF-Tracker is systematically evaluated on both synthetic datasets and real-world scenarios, demonstrating strong generalization across diverse and challenging environments. Experimental results highlight the effectiveness and robustness of PPF-Tracker in multi-frame pose tracking of articulated objects. We believe this work can foster advances in robotics, embodied intelligence, and augmented reality. Codes are available at https://github.com/mengxh20/PPFTracker.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
