{
  "id": "1309.3615",
  "version": "v2",
  "published": "2013-09-14T01:14:18.000Z",
  "updated": "2014-09-17T18:04:29.000Z",
  "title": "Implicit sampling for path integral control, Monte Carlo localization, and SLAM",
  "authors": [
    "Matthias Morzfeld"
  ],
  "categories": [
    "math.OC"
  ],
  "abstract": "The applicability and usefulness of implicit sampling in stochastic optimal control, stochastic localization, and simultaneous localization and mapping (SLAM), is explored; implicit sampling is a recently-developed variationally-enhanced sampling method. The theory is illustrated with examples, and it is found that implicit sampling is significantly more efficient than current Monte Carlo methods in test problems for all three applications.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2013-09-14T01:14:18.000Z",
      "comment": null,
      "journal": null,
      "doi": null
    },
    {
      "version": "v2",
      "updated": "2014-09-17T18:04:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "implicit sampling",
      "path integral control",
      "monte carlo localization",
      "current monte carlo methods",
      "stochastic optimal control"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1309.3615M"
    }
  }
}