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Online probabilistic topological mapping
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OA
AI
DOI：10.1177/0278364910393287.png)
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摘要
En 中文
We present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping (OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given measurements. With each successive measurement, the posterior is updated incrementally using a Rao-Blackwellized particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies that further enable OPTM's operation in an online manner. OPTM can incorporate various sensors seamlessly, as is demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art significantly. We evaluate the algorithm on a robot in diverse environments.
Keyword:
Robot mapping
topological maps
Bayesian estimation
Rao-Blackwellization
particle filtering
appearance modelling
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IF:
5
论文数:
2.4K
被引数:
1.5W
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A probabilistic approach to concurrent mapping and localization for mobile robots.png)
MACHINE LEARNING
MACHINE LEARNINGIF2.9
PREAI
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