{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:13:26Z","timestamp":1760177606663,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,24]],"date-time":"2020-07-24T00:00:00Z","timestamp":1595548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. \u201cSparse semantic\u201d refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.<\/jats:p>","DOI":"10.3390\/s20154128","type":"journal-article","created":{"date-parts":[[2020,7,24]],"date-time":"2020-07-24T11:16:35Z","timestamp":1595589395000},"page":"4128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Robust and Efficient Indoor Localization Using Sparse Semantic Information from a Spherical Camera"],"prefix":"10.3390","volume":"20","author":[{"given":"Irem","family":"Uygur","sequence":"first","affiliation":[{"name":"Department of Precision Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2471-2187","authenticated-orcid":false,"given":"Renato","family":"Miyagusuku","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Intelligent Engineering, Utsunomiya University, Utsunomiya 321-8585, Tochigi, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-1782","authenticated-orcid":false,"given":"Sarthak","family":"Pathak","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Moro","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1280-069X","authenticated-orcid":false,"given":"Atsushi","family":"Yamashita","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hajime","family":"Asama","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-de Ipi\u00f1a, D., Lorido, T., and L\u00f3pez, U. 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