CHSEL：从接触与自由空间数据生成多样化且合理的位姿估计
CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data
AI总结：
提出CHSEL方法，利用接触与自由空间数据估计刚性物体多样化合理位姿，结合可微体积成本函数与质量多样性优化，显著优于现有基线。
摘要：
本文提出了一种新颖方法，用于从具有体积信息（例如每个点位于自由空间还是物体表面）的点集中估计刚性物体的合理位姿集合。特别地，我们研究了如何从接触产生的力和触觉数据中估计位姿。使用源自接触的数据具有挑战性，因为其信息密度本质上低于视觉数据，因此在接触较少时，位姿估计问题严重欠约束。我们不试图估计物体的真实位姿（在没有大量接触的情况下这是不可行的），而是寻求估计满足传感器数据所施加约束的合理位姿集合。现有方法难以估计这一集合，因为它们要么设计用于单一姿态估计，要么需要有效的先验信息才能发挥作用。我们解决此问题的方法——约束位姿假设集消除（CHSEL）——具有三个关键属性：1）它考虑体积信息，使我们能够考虑已知的自由空间；2）它使用新颖的可微体积成本函数，以利用强大的基于梯度的优化工具；3）它使用质量多样性（QD）优化文献中的方法，以生成多样化且高质量的位姿集合。据我们所知，QD方法此前尚未用于位姿配准。我们还展示了如何在机器人收集更多数据时在线更新我们的合理位姿估计。我们的实验表明，在模拟和真实世界数据上，CHSEL相比几种基线方法表现出显著的性能提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
