{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:02:53Z","timestamp":1781298173037,"version":"3.54.1"},"reference-count":44,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"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":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.<\/jats:p>","DOI":"10.1017\/s0263574721000369","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T06:18:11Z","timestamp":1618899491000},"page":"38-55","source":"Crossref","is-referenced-by-count":32,"title":["An efficient LiDAR-based localization method for self-driving cars in dynamic environments"],"prefix":"10.1017","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6341-4057","authenticated-orcid":false,"given":"Yihuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhui","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"key":"S0263574721000369_ref21","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1017\/S0263574720000351","article-title":"Information fusion of GPS, INS and odometer sensors for improving localization accuracy of mobile robots in indoor and outdoor applications","volume":"39","author":"Sofia","year":"2021","journal-title":"Robotica"},{"key":"S0263574721000369_ref15","doi-asserted-by":"crossref","unstructured":"[15] Hata, A. , Osorio, F. and Wolf, D. , \u201cRobust Curb Detection and Vehicle Localization in Urban Environments,\u201d Proceedings of IEEE Intelligent Vehicles Symposium (2014) pp. 1257\u20131262.","DOI":"10.1109\/IVS.2014.6856405"},{"key":"S0263574721000369_ref39","doi-asserted-by":"crossref","unstructured":"[39] Guo, Y. , Wang, H. , Hu, Q. , Liu, H. , Liu, L. and Bennamoun, M. \u201cDeep learning for 3D point clouds: A survey,\u201d IEEE Trans. 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