This paper proposes a novel method for estimating the set of plausible poses of a rigid object from a set of points with volumetric information, such as whether each point is in free space or on the surface of the object. In particular, we study how pose can be estimated from force and tactile data arising from contact. Using data derived from contact is challenging because it is inherently less information-dense than visual data, and thus the pose estimation problem is severely under-constrained when there are few contacts. Rather than attempting to estimate the true pose of the object, which is not tractable without a large number of contacts, we seek to estimate a plausible set of poses which obey the constraints imposed by the sensor data. Existing methods struggle to estimate this set because they are either designed for single pose estimates or require informative priors to be effective. Our approach to this problem, Constrained pose Hypothesis Set Elimination (CHSEL), has three key attributes: 1) It considers volumetric information, which allows us to account for known free space; 2) It uses a novel differentiable volumetric cost function to take advantage of powerful gradient-based optimization tools; and 3) It uses methods from the Quality Diversity (QD) optimization literature to produce a diverse set of high-quality poses. To our knowledge, QD methods have not been used previously for pose registration. We also show how to update our plausible pose estimates online as more data is gathered by the robot. Our experiments suggest that CHSEL shows large performance improvements over several baseline methods for both simulated and real-world data.

翻译：本文提出一种新颖方法，用于根据具有体素信息的一组点（例如每个点位于自由空间还是物体表面）来估计刚性物体的合理姿态集合。我们重点研究了如何从接触产生的力和触觉数据中估计姿态。由于接触数据的信息密度固有地低于视觉数据，因此利用接触数据进行估计极具挑战性：当接触点较少时，姿态估计问题将严重欠约束。我们不试图估计物体的真实姿态（这在缺乏大量接触时难以实现），而是寻求估计一组符合传感器数据约束的合理姿态。现有方法难以估计该集合，因为它们要么专为单一姿态估计设计，要么需要信息丰富的先验知识才能有效工作。我们提出的解决方案——约束姿态假设集消除法（CHSEL）——具有三个关键特性：1）考虑体素信息，从而能够纳入已知的自由空间信息；2）采用新颖的可微分体素代价函数，以利用强大的梯度优化工具；3）利用质量多样性（QD）优化文献中的方法生成多样化且高质量的姿态集合。据我们所知，QD方法此前尚未被用于姿态配准。我们同时展示了如何在机器人收集更多数据时在线更新合理姿态估计。实验表明，无论是在仿真数据还是真实数据上，CHSEL相较于多种基线方法均展现出显著的性能提升。