The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present an approach for efﬁciently recognizing all objects in a scene and estimating their full pose from multiple views. Our approach builds upon a state of the art single-view algorithm which recognizes and registers learned metric 3D models using local descriptors. We extend to multiple views using a novel multi-step optimization that processes each view individually and feeds consistent hypotheses back to the algorithm for global reﬁnement. We demonstrate that our method produces results comparable to the theoretical optimum, a full multi-view generalized camera approach, while avoiding its combinatorial time complexity. We provide experimental results demonstrating pose accuracy, speed, and robustness to model error using a three-camera rig, as well as a physical implementation of the pose output being used by an autonomous robot executing grasps in highly cluttered scenes.

Notes

Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Personal Robotics

Number of pages: 6

Note: Please see the accompanying video at http://www.youtube.com/watch?v=ZNHRH00UMvk

Text Reference

Alvaro Collet Romea and Siddhartha Srinivasa, "Efficient Multi-View Object Recognition and Full Pose Estimation," 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), May, 2010.

BibTeX Reference

@inproceedings{Collet_Romea_2010_6564,
   author = "Alvaro {Collet Romea} and Siddhartha Srinivasa",
   title = "Efficient Multi-View Object Recognition and Full Pose Estimation",
   booktitle = "2010 IEEE International Conference on Robotics and Automation (ICRA 2010)",
   month = "May",
   year = "2010",
   Notes = "Please see the accompanying video at http://www.youtube.com/watch?v=ZNHRH00UMvk"
}

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