Deliberative Object Pose Estimation in Clutter

Abstract

A fundamental robot perception task is that of identifying and estimating the poses of objects with known 3D models in RGB-D data. While feature-based and discriminative approaches have been traditionally used for this task, recent work on deliberative approaches such as PERCH and D2P have shown improved robustness in handling scenes with severe inter-object occlusions. These deliberative approaches work by treating multi-object pose estimation as a combinatorial search over the space of possible rendered scenes of the objects, thereby inherently being able to predict and account for occlusions. However, these methods have so far been restricted to scenes comprising only of known objects, and have been unable to handle extraneous clutter—a common occurrence in many real-world settings. This work significantly increases the practical relevance of deliberative perception methods by developing a formulation that: i) accounts for extraneous unmodeled clutter in scenes, and ii) provides object pose uncertainty estimates. Our algorithm is complete and provides bounded suboptimality guarantees for the cost function chosen to be optimized. Empirically, we demonstrate successful object recognition and uncertainty-aware localization in challenging scenes with unmodeled clutter, where previous deliberative methods perform unsatisfactorily. In addition, this work was used as part of the perception system by Carnegie Mellon University’s Team HARP in the 2016 Amazon Picking Challenge.