Implicit sampling is a recently developed variationally enhanced sampling method that guides its samples to regions of high probability, so that each sample carries information. Implicit sampling may thus improve the performance of algorithms that rely on Monte Carlo (MC) methods. Here the applicability and usefulness of implicit sampling for improving the performance of MC methods in estimation and control is explored, and implicit sampling based algorithms for stochastic optimal control, stochastic localization, and simultaneous localization and mapping (SLAM) are presented. The algorithms are tested in numerical experiments where it is found that fewer samples are required if implicit sampling is used, and that the overall runtimes of the algorithms are reduced.
Implicit Sampling for Path Integral Control, Monte Carlo Localization, and SLAM
University of California, Berkeley;
Berkeley, CA 94720
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 13, 2013; final manuscript received November 6, 2014; published online January 27, 2015. Assoc. Editor: John B. Ferris.
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Morzfeld, M. (January 27, 2015). "Implicit Sampling for Path Integral Control, Monte Carlo Localization, and SLAM." ASME. J. Dyn. Sys., Meas., Control. May 2015; 137(5): 051016. https://doi.org/10.1115/1.4029064
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