基于点对特征的关节物体姿态跟踪框架，提升机器人操作中的定位精度。
Exploring Category-level Articulated Object Pose Tracking on SE(3) Manifolds
- 在SE(3)流形空间中对点云进行准规范处理，利用点对特征建模关节物体
- 通过联合约束所有部件的运动，实现多帧下稳定的姿态追踪
- 适用于真实场景与合成数据，适合机器人、增强现实等应用
关节类物体在日常生活中和机器人操作任务中普遍存在，但因其固有的运动学约束，相比刚性物体，其姿态追踪问题仍研究不足。本文提出一种基于点对特征的新型姿态追踪框架——PPF-Tracker。该框架首先在SE(3)李群空间中对点云进行准规范处理，随后利用点对特征（PPF）建模关节物体，并通过利用SE(3)的不变性特性预测姿态投票参数。最后，融合关节轴的语义信息，对物体各部分施加统一的运动学约束。PPF-Tracker在合成数据集和真实世界场景中进行了系统评估，展现出在多样化且具有挑战性的环境下的强泛化能力。实验结果表明，该方法在多帧关节物体姿态追踪中具备高效性与鲁棒性。我们相信该工作将推动机器人、具身智能与增强现实领域的进步。代码已开源：https://github.com/mengxh20/PPFTracker。
原文摘要 · Abstract (English)
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.
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