{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T10:00:20Z","timestamp":1777716020422,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2010,6,23]],"date-time":"2010-06-23T00:00:00Z","timestamp":1277251200000},"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":[[2011,1]]},"abstract":"<jats:p>In this paper we present a novel method for online and incremental appearance-based localization and mapping in a highly dynamic environment. Using position-invariant robust features (PIRFs), the method can achieve a high rate of recall with 100% precision. It can handle both strong perceptual aliasing and dynamic changes of places efficiently. Its performance also extends beyond conventional images; it is applicable to omnidirectional images for which the major portions of scenes are similar for most places. The proposed PIRF-based Navigation method named PIRF-Nav is evaluated by testing it on two standard datasets in a similar manner as in FAB-MAP and on an additional omnidirectional image dataset that we collected. This extra dataset was collected on 2 days with different specific events, i.e. an open-campus event, to present challenges related to illumination variance and strong dynamic changes, and to test assessment of dynamic scene changes. Results show that PIRF-Nav outperforms FAB-MAP; PIRF-Nav at precision-1 yields a recall rate about twice as high (approximately 80% increase) than that of FAB-MAP. Its computation time is sufficiently short for real-time applications. The method is fully incremental, and requires no offline process for dictionary creation. Additional testing using combined datasets proves that PIRF-Nav can function over the long term and can solve the kidnapped robot problem.<\/jats:p>","DOI":"10.1177\/0278364910371855","type":"journal-article","created":{"date-parts":[[2010,6,23]],"date-time":"2010-06-23T20:24:48Z","timestamp":1277324688000},"page":"33-55","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":55,"title":["Online and Incremental Appearance-based SLAM in Highly Dynamic                 Environments"],"prefix":"10.1177","volume":"30","author":[{"given":"Aram","family":"Kawewong","sequence":"first","affiliation":[{"name":"Department of Computational Intelligence and Systems Science, Tokyo                         Institute of Technology, Yokohama, Japan,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noppharit","family":"Tongprasit","sequence":"additional","affiliation":[{"name":"Department of Computational Intelligence and Systems Science, Tokyo                         Institute of Technology, Yokohama, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sirinart","family":"Tangruamsub","sequence":"additional","affiliation":[{"name":"Department of Computational Intelligence and Systems Science, Tokyo                         Institute of Technology, Yokohama, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Osamu","family":"Hasegawa","sequence":"additional","affiliation":[{"name":"Department of Computational Intelligence and Systems Science, Tokyo                         Institute of Technology, Yokohama, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2010,6,23]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2008.2004514"},{"key":"atypb2","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV)","author":"Bay, 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