{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:20:22Z","timestamp":1770751222187,"version":"3.50.0"},"reference-count":47,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2015,11,21]],"date-time":"2015-11-21T00:00:00Z","timestamp":1448064000000},"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":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,11,27]]},"abstract":"<jats:p>Road detection is still full of challenge for a small ground mobile robot with limited load capacity and computing resources which works in complex outdoor environment. This paper proposes a road detection method based on fuzzy support vector machine with on-line updating and retraining strategy. The algorithm extracts multi feature in image and trains a fuzzy support vector machine road classifier off-line by using few training samples. Then it detects road in laser points using a fuzzy clustering method based on maximum entropy principle. After calibrating the camera and laser range finder, and project laser points into the image, the algorithm chooses road samples with high confidence automatically according to range data and designs a rule to retraining the FSVM on-line when needed to improve its environmental adaptability. Experiments in outdoor campus environment indicate that the proposed algorithm is effective.<\/jats:p>","DOI":"10.3233\/ifs-151971","type":"journal-article","created":{"date-parts":[[2015,12,9]],"date-time":"2015-12-09T14:27:57Z","timestamp":1449671277000},"page":"2677-2688","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Road detection in image by fusion laser points based on fuzzy SVM for a small ground mobile robot"],"prefix":"10.1177","volume":"29","author":[{"given":"Xia","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei, Nanjing, China"}]},{"given":"Xiangyan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei, Nanjing, China"}]},{"given":"Chunxia","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei, Nanjing, China"}]}],"member":"179","published-online":{"date-parts":[[2015,11,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/5254.747906"},{"key":"e_1_3_2_3_2","first-page":"1247","article-title":"Road side detection and reconstruction using LIDAR sensor","author":"Hervieu A","year":"2013","unstructured":"Hervieu A, Soheilian B2013Road side detection and reconstruction using LIDAR sensorIn Proceedings of the IEEE on the Intelligent Vehicles Symposium, Gold Coast12471252","journal-title":"In Proceedings of the IEEE on the Intelligent Vehicles Symposium, Gold Coast"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Geiger A Lenz P Urtasun R2012Are we ready for autonomous driving? 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