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MOPED: A Scalable and Low Latency Object Recognition and Pose Estimation System

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journal contribution
posted on 01.05.2010, 00:00 by Manuel Martinez, Alvaro Collet Romea, Siddhartha Srinivasa

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 30× improvement on real-world scenes.