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

摘要

The robotic perception community has placed significant emphasis on designing and improving perception modules for estimating the poses of objects in a scene. These modules have enormous value for autonomous systems, in that they reduce extremely high-dimensional sensor inputs to compact and semantically loaded object state that can be consumed by a broad range of motion planners and robot controllers.