Calibration of fixtures in robotic work cells is essential but also time consuming and error-prone, and poor calibration can easily lead to wasted debugging time in downstream tasks. Contact-based calibration methods let the user measure points on the fixture's surface with a tool tip attached to the robot's end effector. Most such methods require the user to manually annotate correspondences on the CAD model, however, this is error-prone and a cumbersome user experience. We propose a correspondence-free alternative: The user simply measures a few points from the fixture's surface, and our method provides a tight superset of the poses which could explain the measured points. This naturally detects ambiguities related to symmetry and uninformative points and conveys this uncertainty to the user. Perhaps more importantly, it provides guaranteed bounds on the pose. The computation of such bounds is made tractable by the use of a hierarchical grid on SE(3). Our method is evaluated both in simulation and on a real collaborative robot, showing great potential for easier and less error-prone fixture calibration. Project page at https://sites.google.com/view/ttpose

翻译：机器人工作单元中夹具的标定至关重要，但同时也耗时且易出错，较差的标定容易导致下游任务中调试时间的浪费。基于接触的标定方法允许用户使用安装在机器人末端执行器上的工具尖端测量夹具表面的点。然而，大多数此类方法要求用户在CAD模型上手动标注对应关系，这容易出错且用户体验繁琐。我们提出了一种无需对应关系的替代方案：用户只需从夹具表面测量少量点，我们的方法即可提供一个紧凑的位姿超集，这些位姿均能解释所测量的点。该方法自然地检测出与对称性和非信息性点相关的模糊性，并将这种不确定性传达给用户。或许更重要的是，它提供了位姿的保证边界。通过使用SE(3)上的分层网格，此类边界的计算变得易于处理。我们分别在仿真环境和真实协作机器人上进行了方法评估，结果表明该方法在实现更简单且更不易出错的夹具标定方面具有巨大潜力。项目页面：https://sites.google.com/view/ttpose