Real-Time Object Localization for Human-Robot Handover

Sadjad Asghari-Esfeden, Garrit Strenge, Kyle Lockwood, Yunus Bicer, Tales Imbiriba, Mariusz P. Furmanek, Mathew Yarossi, Eugene Tunik, Taskin Padir, Deniz Erdogmus

Abstract: Human-robot interaction in a physical world like handover of objects requires perception systems to be efficient in localizing the object of interest. In this paper we propose an approach to estimate the location of the object with a low-cost RGB camera in a real-time fashion for human-robot handover. While handover can take place in a short amount of time, it is important for a robot to keep track of the object and fill in the gaps of missing detections in the perception module, specially when the object is partially or completely occluded. A robot needs to proactively detect and track the object since the human decides where and when to transfer the object to the robot in a human to robot object handover. In order to develop a perception system for robot to be capable of constantly localizing the object and predict its location and time of transfer, we integrate an object detection algorithm with a tracking framework. The evaluation of this pipeline shows promising results for the goal of localization and tracking of the handover object and can help its location prediction in future.