Realtime perception for catching a flying ball with a mobile humanoid

Abstract

This paper presents a realtime perception system for catching flying balls with DLR's humanoid Rollin' Justin. We use a two-staged bottom up approach in which we first detect balls as circles and feed these measurements into a multiple hypothesis tracker (MHT). The novel circle detection scheme works in realistic scenes without tuning parameters or background assumptions. We extend the classical multi-hypothesis tracking with prior information about the expected trajectories, therefore limiting the number of hypotheses in the first place. Since the robot starts moving while the ball is still tracked, the cameras shake heavily. A 6-DOF inertial measurements unit (IMU) is integrated to compensate this motion. Using ground-truth from a marker based tracking system we evaluate the metrical accuracy of the motion compensation as well as the tracker's prediction accuracy while in motion.