MOPED: A Scalable and Low Latency Object Recognition and Pose Estimation System
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, pp. 2043 - 2049, May, 2010
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.
BibTeX
@conference{Torres-2010-10436,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) International Conference on Robotics and Automation},
year = {2010},
month = {May},
pages = {2043 - 2049},
}
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