Globally Optimal Object Pose Estimation in Point Clouds with Mixed‑Integer Programming

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TRI Authors: Hongkai Dai, Russ Tedrake All Authors: Gregory Izatt, Hongkai Dai, Russ Tedrake Motivated by the limitations of local object trackers, we present a formulation of the underlying point-cloud object pose estimation problem as a mixed-integer convex program, which we efficiently solve to optimality with an off-the-shelf branch and bound solver. We show that reasoning about object pose estimation in this way allows natural extension to point-to-mesh correspondence, multiple simultaneous object pose estimation, and outlier rejection without losing the ability to obtain a globally optimal solution. We probe the extent to which rich problem-specific formulations typically tackled with unreliable nonlinear optimization can be rigorously treated in a global optimization framework to overcome the limitations of other global pose estimation methods. Read more Citation: Izatt, Gregory, Hongkai Dai, and Russ Tedrake. "Globally optimal object pose estimation in point clouds with mixed-integer programming." International Symposium on Robotics Research. Vol. 12. 2017.