IROS 2015 Paper Abstract


Paper TuFT7.2

Holz, Dirk (University of Bonn), Topalidou-Kyniazopoulou, Angeliki (University of Bonn), Stueckler, Joerg (Technical University Munich), Behnke, Sven (University of Bonn)

Real-Time Object Detection, Localization and Verification for Fast Robotic Depalletizing

Scheduled for presentation during the Regular session "Mobile Manipulation" (TuFT7), Tuesday, September 29, 2015, 17:05−17:20, Saal C1+C2

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Mobile Manipulation, Perception for Grasping and Manipulation, Industrial Robots


Depalletizing is a challenging task for manipulation robots. Key to successful application are not only robustness of the approach, but also achievable cycle times in order to keep up with the rest of the process. In this paper, we propose a system for depalletizing and a complete pipeline for detecting and localizing objects as well as verifying that the found object does not deviate from the known object model, e.g., if it is not the object to pick. In order to achieve high robustness (e.g., with respect to different lighting conditions) and generality with respect to the objects to pick, our approach is based on multi-resolution surfel models. All components (both software and hardware) allow operation at high frame rates and, thus, allow for low cycle times.

In experiments, we demonstrate depalletizing of automotive and other prefabricated parts with both high reliability (w.r.t. success rates) and efficiency (w.r.t. low cycle times).



Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2020 PaperCept, Inc.
Page generated 2020-07-14  16:22:18 PST  Terms of use