{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T22:34:30Z","timestamp":1775601270559,"version":"3.50.1"},"reference-count":38,"publisher":"Cambridge University Press (CUP)","issue":"11","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In firefighting missions, human firefighters are often exposed to high-risk environments such as intense heat and limited visibility. To address this, firefighting robots can serve as valuable agents for autonomous navigation and flame perception. This paper proposes a novel visual Simultaneous Localization and Mapping (SLAM) framework, Fire\n                    <jats:italic>SLAM<\/jats:italic>\n                    , tailored for firefighting scenarios. The system integrates a flame detection and tracking thread-based on the YOLOv8n network and Kalman filtering-to achieve real-time flame detection, tracking, and 3D localization. By leveraging the detection results, dynamic flame regions are excluded from the SLAM front-end, allowing static features to be used for robust pose estimation and loop closure. To validate the proposed system, multiple datasets were collected from real-world and simulated fire environments. Experimental results demonstrate that Fire SLAM improves localization accuracy and robustness in fire scenes with flame disturbances, showing promise for autonomous firefighting robot deployment.\n                  <\/jats:p>","DOI":"10.1017\/s0263574725102580","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T08:49:31Z","timestamp":1761554971000},"page":"4116-4132","source":"Crossref","is-referenced-by-count":1,"title":["Fire SLAM: a visual Simultaneous Localization and Mapping algorithm for firefighting robots"],"prefix":"10.1017","volume":"43","author":[{"given":"Tao","family":"Yang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02txfnf15","id-type":"ROR","asserted-by":"publisher"}],"name":"Yanshan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1671-7789","authenticated-orcid":false,"given":"Weili","family":"Ding","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02txfnf15","id-type":"ROR","asserted-by":"publisher"}],"name":"Yanshan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Luo","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02txfnf15","id-type":"ROR","asserted-by":"publisher"}],"name":"Yanshan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"S0263574725102580_ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-017-1102-y"},{"key":"S0263574725102580_ref26","doi-asserted-by":"publisher","DOI":"10.1109\/I2CT.2017.8226309"},{"key":"S0263574725102580_ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2990224"},{"key":"S0263574725102580_ref36","doi-asserted-by":"publisher","DOI":"10.1145\/272991.272995"},{"key":"S0263574725102580_ref37","unstructured":"[37] Hinneburg, A. , \u201cA Density Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. 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