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.

翻译：机器人描述模型对于仿真与控制至关重要，但其创建通常需要大量人工投入。为简化建模流程，本文提出AutoURDF——一种从点云帧为未知机器人构建描述文件的无监督方法。本方法采用基于聚类的点云配准模型，以追踪点云簇的六自由度变换。通过分析点云簇的运动，我们分层解决了以下挑战：(1) 运动部件分割，(2) 本体拓扑结构推断，以及(3) 关节参数估计。完整流程生成的机器人描述文件与现有仿真器完全兼容。我们在多种机器人上使用合成数据与真实扫描数据验证了本方法。结果表明，该方法在配准精度与本体拓扑估计准确度上均优于现有方法，为自动化机器人建模提供了可扩展的解决方案。