2010 IEEE International Conference on Robotics and Automation :: Anchorage, Alaska, USA :: May 3 - 8, 2010
   

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Paper TuA10.1

Sturm, Jürgen (University of Freiburg), Konolige, Kurt (Willow Garage), Stachniss, Cyrill (University of Freiburg), Burgard, Wolfram (University of Freiburg)

Vision-Based Detection for Learning Articulation Models of Cabinet Doors and Drawers in Household Environments

Scheduled for presentation during the Regular Sessions "AI Reasoning Methods" (TuA10), Tuesday, May 4, 2010, 08:30−08:45, Egan Center Lower Level Room 9/10

2010 IEEE International Conference on Robotics and Automation, May 3-8, 2010, Anchorage, Alaska, USA

This information is tentative and subject to change. Compiled on April 16, 2014

Keywords Domestic Robots, Recognition, Mobile Manipulation

Abstract

Service robots deployed in domestic environments generally need the capability to deal with articulated objects such as doors and drawers in order to fulfill certain mobile manipulation tasks. This however, requires, that the robots are able to perceive the articulation models of such objects. In this paper, we present an approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers. Our approach uses a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system. The robot can use the generative models learned for the articulated objects to estimate their articulation type, their current configuration, and to make predictions about possible configurations not observed before. We present experiments carried out on real data obtained from our active stereo system. The results demonstrate that our technique is able to learn accurate articulation models. We furthermore provide a detailed error analysis based on ground truth data obtained in a motion capturing studio.

 

 

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