{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:23:56Z","timestamp":1754155436361,"version":"3.41.2"},"reference-count":34,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2014,5,13]],"date-time":"2014-05-13T00:00:00Z","timestamp":1399939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,5,13]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 The purpose of this paper is to propose a localizability-based particle filtering localization algorithm for mobile robots to maintain localization accuracy in the high-occluded and dynamic environments with moving people. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 First, the localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Second, based on the classical two-sensor track fusion algorithm, the odometer-based proposal distribution function (PDF) is corrected, taking account of the localizability. Then, the corrected PDF is introduced into the classical PF with \u201croulette\u201d re-sampling. Finally, the robot pose is estimated according to all the particles. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The experimental results show that, first, it is necessary to consider the influence of the prior-map during the localization in the high-occluded and dynamic environments. Second, the proposed algorithm can maintain an accurate and robust robot pose in the high-occluded and dynamic environments. Third, its real timing is acceptable. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title>\n               <jats:p> \u2013 When the odometer error and occlusion caused by the dynamic obstacles are both serious, the proposed algorithm also has a probability evolving into the kidnap problem. But fortunately, such serious situations are not common in practice. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title>\n               <jats:p> \u2013 To check the ability of real application, we have implemented the proposed algorithm in the campus cafeteria and metro station using an intelligent wheelchair. To better help the elderly and disabled people during their daily lives, the proposed algorithm will be tested in a social welfare home in the future. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Original\/value<\/jats:title>\n               <jats:p> \u2013 The localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Based on the localizability, the odometer-based PDF is corrected properly.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/ir-06-2013-371","type":"journal-article","created":{"date-parts":[[2014,7,10]],"date-time":"2014-07-10T10:31:06Z","timestamp":1404988266000},"page":"241-252","source":"Crossref","is-referenced-by-count":11,"title":["Map-based localization for mobile robots in high-occluded and dynamic environments"],"prefix":"10.1108","volume":"41","author":[{"given":"Yong","family":"Wang","sequence":"first","affiliation":[]},{"given":"Weidong","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jingchuan","family":"Wang","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2021010100180089200_b1","doi-asserted-by":"crossref","unstructured":"Antonelli, G.\n               , \n                  Chiaverini, S.\n                and \n                  Fusco, G.\n                (2005), \u201cA calibration method for odometry of mobile robots based on the least-squares technique: theory and experimental validation\u201d, IEEE Transactions on Robotics, Vol. 21 No. 5, pp. 994-1004.","DOI":"10.1109\/TRO.2005.851382"},{"key":"key2021010100180089200_b2","unstructured":"Bar-Shalom, Y.\n                and \n                  Fortmann, T.E.\n                (1987), Tracking and Data Association, Academic Press Professional, San Diego, CA."},{"key":"key2021010100180089200_b3","doi-asserted-by":"crossref","unstructured":"Bar-Shalom, Y.\n               , \n                  Li, X.R.\n                and \n                  Kirubarajan, T.\n                (2001), Estimation with Applications to Tracking and Navigation, John Wiley & Sons, New York, NY.","DOI":"10.1002\/0471221279"},{"key":"key2021010100180089200_b4","unstructured":"Bergman, N.\n                (1999), \u201cRecursive Bayesian estimation: navigation and tracking applications\u201d, PhD dissertation, Link\u00f6ping University, Link\u00f6ping."},{"key":"key2021010100180089200_b5","doi-asserted-by":"crossref","unstructured":"Bobrovsky, B.Z.\n                and \n                  Zakai, M.\n                (1975), \u201cA lower bound on the estimation error for markov processes\u201d, IEEE Transactions on Automatic Control, Vol. 20 No. 6, pp. 785-788.","DOI":"10.1109\/TAC.1975.1101088"},{"key":"key2021010100180089200_b6","unstructured":"Borenstein, J.\n               , \n                  Everett, B.\n                and \n                  Feng, L.\n                (1996), Navigating Mobile Robots: Systems and Techniques, A.K. 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