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Carnegie Mellon Robotics Institute

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Abstract

The latency of a perception system is crucial for a robot performing interactive tasks in dynamic human environments. We present MOPED, a fast and scalable perception system for object recognition and pose estimation. MOPED builds on POSESEQ, a state of the art object recognition algorithm, demonstrating a massive improvement in scalability and latency without sacriﬁcing robustness. We achieve this with both algorithmic and architecture improvements, with a novel feature matching algorithm, a hybrid GPU/CPU architecture that exploits parallelism at all levels, and an optimized resource scheduler. Using the same standard hardware, we achieve up to 30x improvement on real-world 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: 7

Text Reference

Manuel Martinez Torres, Alvaro Collet Romea, and Siddhartha Srinivasa, "MOPED: A Scalable and Low Latency Object Recognition and Pose Estimation System ," Proceedings of ICRA 2010, May, 2010.

BibTeX Reference

@inproceedings{Martinez_Torres_2010_6543,
   author = "Manuel {Martinez Torres} and Alvaro {Collet Romea} and Siddhartha Srinivasa",
   title = "MOPED: A Scalable and Low Latency Object Recognition and Pose Estimation System ",
   booktitle = "Proceedings of ICRA 2010",
   month = "May",
   year = "2010",
}

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