{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:04:26Z","timestamp":1760241866869,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T00:00:00Z","timestamp":1540339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key RD Program of China","award":["2016YFE0206200"],"award-info":[{"award-number":["2016YFE0206200"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1613205, 51675291"],"award-info":[{"award-number":["U1613205, 51675291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of China","award":["SKLT2018C04"],"award-info":[{"award-number":["SKLT2018C04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Point cloud registration plays a key role in three-dimensional scene reconstruction, and determines the effect of reconstruction. The iterative closest point algorithm is widely used for point cloud registration. To improve the accuracy of point cloud registration and the convergence speed of registration error, point pairs with smaller Euclidean distances are used as the points to be registered, and the depth measurement error model and weight function are analyzed. The measurement error is taken into account in the registration process. The experimental results of different indoor scenes demonstrate that the proposed method effectively improves the registration accuracy and the convergence speed of registration error.<\/jats:p>","DOI":"10.3390\/s18113608","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T10:40:48Z","timestamp":1540377648000},"page":"3608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Depth-Based Weighted Point Cloud Registration for Indoor Scene"],"prefix":"10.3390","volume":"18","author":[{"given":"Shuntao","family":"Liu","sequence":"first","affiliation":[{"name":"AVIC Chengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dedong","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Qinghai University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Qinghai University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tribology and Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tribology and Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Du-Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tribology and Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,24]]},"reference":[{"key":"ref_1","unstructured":"Pulli, K. (1999, January 4\u20138). Multiview registration for large data sets. Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling, YOW, Gloucester, ON, Canada."},{"key":"ref_2","unstructured":"Matabosch, C., and Salvi, J. (2007, January 23\u201325). Overview of 3D registration techniques including loop minimization for the complete acquisition of large manufactured parts and complex environments. Proceedings of the Eighth International Conference on Quality Control by Artificial Vision, Le Creusot, France."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13634-016-0435-y","article-title":"A novel point cloud registration using 2d image features","volume":"1","author":"Lin","year":"2017","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12\u201317). Fast point feature histograms (FPFH) for 3D registration. 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