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This paper proposes a novel method for estimating the set of plausible posesof a rigid object from a set of points with volumetric information, such aswhether each point is in free space or on the surface of the object. Inparticular, we study how pose can be estimated from force and tactile dataarising from contact. Using data derived from contact is challenging because itis inherently less information-dense than visual data, and thus the poseestimation problem is severely under-constrained when there are few contacts.Rather than attempting to estimate the true pose of the object, which is nottractable without a large number of contacts, we seek to estimate a plausibleset of poses which obey the constraints imposed by the sensor data. Existingmethods struggle to estimate this set because they are either designed forsingle pose estimates or require informative priors to be effective. Ourapproach to this problem, Constrained pose Hypothesis Set Elimination (CHSEL),has three key attributes: 1) It considers volumetric information, which allowsus to account for known free space; 2) It uses a novel differentiablevolumetric cost function to take advantage of powerful gradient-basedoptimization tools; and 3) It uses methods from the Quality Diversity (QD)optimization literature to produce a diverse set of high-quality poses. To ourknowledge, QD methods have not been used previously for pose registration. Wealso show how to update our plausible pose estimates online as more data isgathered by the robot. Our experiments suggest that CHSEL shows largeperformance improvements over several baseline methods for both simulated andreal-world data.

CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data