{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:59:26Z","timestamp":1791478766830,"version":"4.3.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2017,3,1]],"date-time":"2017-03-01T00:00:00Z","timestamp":1488326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100002427","name":"Ford Motor Company","doi-asserted-by":"publisher","award":["N015392"],"award-info":[{"award-number":["N015392"]}],"id":[{"id":"10.13039\/100002427","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2017,3]]},"abstract":"<jats:p>This paper reports on a fast multiresolution scan matcher for local vehicle localization of self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a 3D light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint (e.g. puddles and snowdrifts), poor road surface texture, or when road appearance degrades over time. We present a generic probabilistic method for localizing an autonomous vehicle equipped with a three-dimensional (3D) LIDAR scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the [Formula: see text]-height and reflectivity distribution of the environment\u2014which we rasterize to facilitate fast and exact multiresolution inference. Results are shown on a collection of datasets totaling over 500 km of road data covering highway, rural, residential, and urban roadways, in which we demonstrate our method to be robust through heavy snowfall and roadway repavements.<\/jats:p>","DOI":"10.1177\/0278364917696568","type":"journal-article","created":{"date-parts":[[2017,4,4]],"date-time":"2017-04-04T08:52:34Z","timestamp":1491295954000},"page":"292-319","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":159,"title":["Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving"],"prefix":"10.1177","volume":"36","author":[{"given":"Ryan W","family":"Wolcott","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Division, University of Michigan, USA"},{"name":"Ford Motor Company, Dearborn, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan M","family":"Eustice","sequence":"additional","affiliation":[{"name":"Department of Naval Architecture & Marine Engineering, University of Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2017,4,4]]},"reference":[{"key":"bibr1-0278364917696568","volume-title":"Proceedings of the European conference on mobile robots","author":"Aghamohammadi AA","year":"2007"},{"key":"bibr2-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1145\/1360612.1360684"},{"key":"bibr3-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2012.6224996"},{"key":"bibr4-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/34.121791"},{"key":"bibr5-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2003.1249285"},{"key":"bibr6-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2013.6630777"},{"key":"bibr7-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-47979-1_28"},{"key":"bibr8-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.223"},{"key":"bibr9-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2008.2006706"},{"key":"bibr10-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2014.6942800"},{"key":"bibr11-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20245"},{"key":"bibr12-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45118-8_53"},{"key":"bibr13-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2010.5509700"},{"key":"bibr14-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-28572-1_13"},{"key":"bibr15-0278364917696568","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2007.III.016"},{"key":"bibr16-0278364917696568","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139414"},{"key":"bibr17-0278364917696568","unstructured":"Magnusson M (2009) The three-dimensional normal-distributions transform\u2014an efficient representation for registration, surface analysis, and loop detection. 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