{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:37:58Z","timestamp":1760488678306,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,3,28]],"date-time":"2016-03-28T00:00:00Z","timestamp":1459123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The registration of long-strip, terrestrial laser scanning (TLS) point clouds is a prerequisite for various engineering tasks, including tunnels, bridges, and roads. An artificial target-based registration method is proposed in this paper to automatically calculate registration parameters (i.e., rotation, translation) of scanned pairs without initial estimations. The approach is based on the well-known Random Sample Consensus (RANSAC) method and effectively searches the point cloud for corresponding returns from a system of artificial targets. In addition, Closed Constraint Adjustment (CCA) is integrated into the registration method to significantly reduce the accumulative error. Experimental results demonstrate the robustness and feasibility of the proposed approach. It is a promising approach to register automatically long strips with limited external control points with satisfactory precision.<\/jats:p>","DOI":"10.3390\/rs8040278","type":"journal-article","created":{"date-parts":[[2016,3,29]],"date-time":"2016-03-29T16:00:28Z","timestamp":1459267228000},"page":"278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Registration of Long-Strip Terrestrial Laser Scanning Point Clouds Using RANSAC and Closed Constraint Adjustment"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-2443","authenticated-orcid":false,"given":"Li","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6769-7517","authenticated-orcid":false,"given":"Manzhu","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Geography and GeoInformation Sciences, College of Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Mengxiao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8165-0667","authenticated-orcid":false,"given":"Anthony","family":"Stefanidis","sequence":"additional","affiliation":[{"name":"Department of Geography and GeoInformation Sciences, College of Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Zheng","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Department of Geography &amp; Environmental Management, University of Waterloo, ON N2L3G1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7768-4066","authenticated-orcid":false,"given":"Chaowei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and GeoInformation Sciences, College of Science, George Mason University, Fairfax, VA 22030, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s40572-014-0019-7","article-title":"Air pollution in the mega-cities","volume":"1","author":"Krzyzanowski","year":"2014","journal-title":"Curr. 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