Laser-based perception for door and handle identification

Abstract

In this paper, we present a laser-based approach for door and handle identification. The approach builds on a 3D perception pipeline to annotate doors and their handles solely from sensed laser data, without any a priori model learning. In particular, we segment the parts of interest using robust geometric estimators and statistical methods applied on geometric and intensity distribution variations in the scan. We present experimental results on a mobile manipulation platform (PR2) intended for indoor manipulation tasks. We validate the approach by generating trajectories that position the robot end-effector in front of door handles and grasp the handle. The robustness of our approach is demonstrated by real world experiments conducted on a large set of doors.