{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:51:23Z","timestamp":1777715483328,"version":"3.51.4"},"reference-count":36,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2015,4,28]],"date-time":"2015-04-28T00:00:00Z","timestamp":1430179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2016,4]]},"abstract":"<jats:p>This paper deals with the task of appearance-based mapping and place recognition. Previously, the scope of a location generally varied between either using discrete poses or loosely defined sequences of poses, facing problems related to perceptual aliasing and path invariance respectively. Here, we present a unified framework for defining, modelling and recognizing places in a way which is directly related to the underlying structure of features in the environment. A covisibility map of the environment is incrementally maintained over time, where visual landmarks represent nodes in a graph which are connected if seen together. When queried, relevant places are retrieved as clusters from this map, and a novel probabilistic observation model is used to evaluate place recognition. Place retrieval is able to adapt to a given query and also inherently cope with trajectory variations, due to the use of the landmark covisibility structure. In addition, the chosen generative model is developed in a way which is robust to observation errors, mapping errors, perceptual aliasing, and parameter sensitivity. Validation is provided through a variety of tests using real-world datasets, which compare the behaviour of the proposed approach to other representative state-of-the-art methods (namely FAB-MAP and SeqSLAM).<\/jats:p>","DOI":"10.1177\/0278364915570140","type":"journal-article","created":{"date-parts":[[2015,4,28]],"date-time":"2015-04-28T21:41:57Z","timestamp":1430257317000},"page":"334-356","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":22,"title":["Building Location Models for Visual Place Recognition"],"prefix":"10.1177","volume":"35","author":[{"given":"Elena S.","family":"Stumm","sequence":"first","affiliation":[{"name":"CNRS, LAAS, Toulouse, France"},{"name":"Universit\u00e9 de Toulouse, UPS, LAAS, Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Mei","sequence":"additional","affiliation":[{"name":"CNRS, LAAS, Toulouse, France"},{"name":"Universit\u00e9 de Toulouse, LAAS, Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Lacroix","sequence":"additional","affiliation":[{"name":"CNRS, LAAS, Toulouse, France"},{"name":"Universit\u00e9 de Toulouse, LAAS, Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2015,4,28]]},"reference":[{"key":"bibr1-0278364915570140","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2008.2004514"},{"key":"bibr2-0278364915570140","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2012.6224716"},{"key":"bibr3-0278364915570140","doi-asserted-by":"crossref","unstructured":"Bay H, Ess A, Tuytelaars T, Van Gool L (2008) SURF: Speeded Up Robust Features. 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