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AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration
- 基于聚类点云配准，追踪点簇的6自由度运动
- 在真实与合成数据上均实现更高拓扑与配准精度
- 适合快速构建未见过机器人的仿真模型
机器人描述模型对仿真与控制至关重要，但传统创建方式依赖大量人工。为简化流程，我们提出AutoURDF，一种从点云帧无监督构建未知机器人描述文件的方法。该方法基于聚类点云配准模型，追踪点簇的6-DoF变换，通过分析其运动，分层解决三方面挑战：（1）运动部件分割，（2）机体拓扑推断，（3）关节参数估计。完整流程生成可兼容现有模拟器的机器人描述文件。我们在多种机器人上验证了该方法，使用合成与真实扫描数据。结果表明，本方法在配准与拓扑估计准确率上优于以往方法，提供了一种可扩展的自动化机器人建模方案。
原文摘要 · Abstract (English)
Robot description models are essential for simulation and control, yet their creation often requires significant manual effort. To streamline this modeling process, we introduce AutoURDF, an unsupervised approach for constructing description files for unseen robots from point cloud frames. Our method leverages a cluster-based point cloud registration model that tracks the 6-DoF transformations of point clusters. Through analyzing cluster movements, we hierarchically address the following challenges: (1) moving part segmentation, (2) body topology inference, and (3) joint parameter estimation. The complete pipeline produces robot description files that are fully compatible with existing simulators. We validate our method across a variety of robots, using both synthetic and real-world scan data. Results indicate that our approach outperforms previous methods in registration and body topology estimation accuracy, offering a scalable solution for automated robot modeling.
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