{"paper":{"title":"Markov Localization for Mobile Robots in Dynamic Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"D. Fox, S. Thrun, W. Burgard","submitted_at":"2011-06-01T16:16:48Z","abstract_excerpt":"Localization, that is the estimation of a robot's location    from sensor data, is a fundamental problem in mobile robotics.  This    papers presents a version of Markov localization which provides    accurate position estimates and which is tailored towards dynamic    environments. The key idea of Markov localization is to maintain a    probability density over the space of all locations of a robot in its    environment. Our approach represents this space metrically, using a    fine-grained grid to approximate densities.  It is able to globally    localize the robot from scratch and to recove"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.0222","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}