2010
AISTATS
AISTATS 2010
A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Abstract
A Markov-Chain Monte Carlo based algorithm is provided to solve the simultaneous localization and mapping (SLAM) problem with general dynamical and observation models under open-loop control and provided that the map-representation is finite dimensional. To our knowledge this is the first provably consistent yet (close-to) practical solution to this problem. The superiority of our algorithm over alternative SLAM algorithms is demonstrated in a difficult loop closing situation.
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Conference Pioneer
— AISTATS 2010
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Trend Setter
— Autonomous Vehicles
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Keyword Pioneer
— map representation
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Hot Topic Early Bird
— markov chain monte carlo
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio