FastSLAM: a factored solution to the simultaneous localization and mapping problem
AAAI/IAAI(2002)
摘要
The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.
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关键词
full posterior distribution,kalman filter-based algorithm,autonomous robot,simultaneous localization,factored solution,conditional landmark distribution,landmarks present,fastslam algorithm,large number,landmark location,exact factorization,mapping problem,robot path,kalman filter,bayesian networks,posterior distribution,simultaneous localization and mapping
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