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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, adoi:10.1109/robot.2010.5509801 dblp:conf/icra/MartinezCS10 fatcat:qf6vu2iymfbmlgggrkqsc5a6ey