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The applicability and usefulness of implicit sampling in stochastic optimalcontrol, stochastic localization, and simultaneous localization and mapping(SLAM), is explored; implicit sampling is a recently-developedvariationally-enhanced sampling method. The theory is illustrated withexamples, and it is found that implicit sampling is significantly moreefficient than current Monte Carlo methods in test problems for all threeapplications.

Implicit sampling for path integral control, Monte Carlo localization, and SLAM