{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T04:12:00Z","timestamp":1773547920804,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Research Program of Jiangsu Colleges and Universities","award":["20KJA470002"],"award-info":[{"award-number":["20KJA470002"]}]},{"name":"Natural Science Research Program of Jiangsu Colleges and Universities","award":["JC2020094"],"award-info":[{"award-number":["JC2020094"]}]},{"name":"Natural Science Research Program of Jiangsu Colleges and Universities","award":["MS22020022"],"award-info":[{"award-number":["MS22020022"]}]},{"name":"Excellent Teaching Team of \u201cQinglan Project\u201d of Jiangsu Colleges and Universities","award":["20KJA470002"],"award-info":[{"award-number":["20KJA470002"]}]},{"name":"Excellent Teaching Team of \u201cQinglan Project\u201d of Jiangsu Colleges and Universities","award":["JC2020094"],"award-info":[{"award-number":["JC2020094"]}]},{"name":"Excellent Teaching Team of \u201cQinglan Project\u201d of Jiangsu Colleges and Universities","award":["MS22020022"],"award-info":[{"award-number":["MS22020022"]}]},{"name":"Science and Technology Research Program of Nantong","award":["20KJA470002"],"award-info":[{"award-number":["20KJA470002"]}]},{"name":"Science and Technology Research Program of Nantong","award":["JC2020094"],"award-info":[{"award-number":["JC2020094"]}]},{"name":"Science and Technology Research Program of Nantong","award":["MS22020022"],"award-info":[{"award-number":["MS22020022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The point clouds registration is a key step in data processing for the 3D laser scanner to obtain complete information of the object surface, and there are many algorithms. In order to overcome the disadvantages of slow calculation speed and low accuracy of existing point clouds registration algorithms, a fast point clouds registration algorithm based on the improved voxel filter and ISS-USC feature is proposed. Firstly, the improved voxel filter is used for down-sampling to reduce the size of the original point clouds data. Secondly, the intrinsic shape signature (ISS) feature point detection algorithm is used to extra feature points from the down-sampled point clouds data, and then the unique shape context (USC) descriptor is calculated to describe the extracted feature points. Next, the improved random sampling consensus (RANSAC) algorithm is used for coarse registration to obtain the initial position. Finally, the iterative closest point (ICP) algorithm based on KD tree is used for fine registration, which realizes the transform from the point clouds scanned by the 3D laser scanner at different angles to the same coordinate system. Through comparing with other algorithms and the registration experiment of the VGA connector for monitor, the experimental results verify the effectiveness and feasibility of the proposed algorithm, and it has fastest registration speed while maintaining high registration accuracy.<\/jats:p>","DOI":"10.3390\/a15100389","type":"journal-article","created":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T20:43:50Z","timestamp":1666557830000},"page":"389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner"],"prefix":"10.3390","volume":"15","author":[{"given":"Aihua","family":"Wu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Nantong University, Nantong 226019, China"}]},{"given":"Yinjia","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nantong University, Nantong 226019, China"}]},{"given":"Jingfeng","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nantong University, Nantong 226019, China"}]},{"given":"Xudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nantong University, Nantong 226019, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109963","DOI":"10.1016\/j.measurement.2021.109963","article-title":"Registration strategy of point clouds based on region-specific projections and virtual structures for robot-based inspection systems","volume":"185","author":"Bauer","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5255","DOI":"10.1016\/j.matpr.2021.01.828","article-title":"A case study on use of 3D scanning for reverse engineering and quality control","volume":"45","author":"Helle","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105008","DOI":"10.1016\/j.ijrmms.2021.105008","article-title":"A new statistical method to segment photogrammetry data in order to obtain geological information","volume":"150","author":"Yazdanpanah","year":"2022","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108250","DOI":"10.1016\/j.dib.2022.108250","article-title":"Data for 3D reconstruction and point cloud classification using machine learning in cultural heritage environment","volume":"42","author":"Pepe","year":"2022","journal-title":"Data Brief"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.isprsjprs.2020.03.013","article-title":"Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark","volume":"163","author":"Dong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.robot.2018.07.003","article-title":"CICP: Cluster Iterative Closest Point for Sparse-Dense Point Cloud Registration","volume":"108","author":"Lamine","year":"2018","journal-title":"Robot. Auton. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TPAMI.2015.2513405","article-title":"Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration","volume":"38","author":"Yang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neucom.2014.03.035","article-title":"LieTrICP: An improvement of trimmed iterative closest point algorithm","volume":"140","author":"Dong","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100120","DOI":"10.1109\/ACCESS.2020.2995369","article-title":"A Local Feature Descriptor Based on Rotational Volume for Pairwise Registration of Point Clouds","volume":"8","author":"Xiong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103064","DOI":"10.1016\/j.micpro.2020.103064","article-title":"3D Scene Reconstruction based on improved ICP algorithm","volume":"75","author":"Wu","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shen, X., Ge, Z., Gao, Q., Sun, H., Tang, X., and Cai, Q. (2022, January 20\u201322). A point cloud registration algorithm for the fusion of virtual and real maintainability test prototypes. Proceedings of the 2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT), Qingdao, China.","DOI":"10.1109\/CNIOT55862.2022.00015"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/rob.20204","article-title":"Scan registration for autonomous mining vehicles using 3D-NDT","volume":"24","author":"Magnusson","year":"2007","journal-title":"J. Field Robot."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.isprsjprs.2017.06.011","article-title":"Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets","volume":"130","author":"Ge","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2022.07.082","article-title":"Iterative BTreeNet: Unsupervised Learning for Large and Dense 3D Point Cloud Registration","volume":"506","author":"Long","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s13634-017-0483-y","article-title":"3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints","volume":"2017","author":"Ghorpade","year":"2017","journal-title":"Eurasip J. Adv. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Stefano, L.D. (2010, January 25). Unique shape context for 3d data description. Proceedings of the ACM workshop on 3D object retrieval DEIS\/ARCES University of Bologna Bologna, Firenze, Italy.","DOI":"10.1145\/1877808.1877821"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103608","DOI":"10.1016\/j.csi.2021.103608","article-title":"Efficient plane extraction using normal estimation and RANSAC from 3D point cloud","volume":"82","author":"Yang","year":"2022","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jvcir.2017.01.013","article-title":"Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching","volume":"44","author":"Hu","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1016\/j.joen.2021.06.011","article-title":"Influence of Voxel Size and Filter Application in Detecting Second Mesiobuccal Canals in Cone-beam Computed Tomographic Images","volume":"47","author":"Rosado","year":"2021","journal-title":"J. Endod."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, H., Liu, X., Deng, Q., Jiang, W., Luo, S., and Ha, Y. (2020). Efficient FPGA Implementation of K-Nearest-Neighbor Search Algorithm for 3D LIDAR Localization and Mapping in Smart Vehicles. Circuits and Systems II: Express Briefs, IEEE.","DOI":"10.1109\/TCSII.2020.3013758"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.cag.2022.02.010","article-title":"Dual spin-image: A bi-directional spin-image variant using multi-scale radii for 3D local shape description","volume":"103","author":"Bibissi","year":"2022","journal-title":"Comput. Graph."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.patcog.2016.02.003","article-title":"Fisher encoding of differential fast point feature histograms for partial 3D object retrieval","volume":"55","author":"Savelonas","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_24","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. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103164","DOI":"10.1016\/j.cad.2021.103164","article-title":"Raw Scanned Point Cloud Registration with Repetition for Aircraft Fuel Tank Inspection","volume":"144","author":"Cao","year":"2022","journal-title":"Comput. Aided Des."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Price, M., Green, J., and Dickens, J. (2012, January 26\u201327). Point-cloud registration using 3D shape contexts. Proceedings of the 2012 5th Robotics and Mechatronics Conference of South Africa, Johannesbe, South Africa.","DOI":"10.1109\/ROBOMECH.2012.6558468"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, G., Pang, Y., Bai, Z., Wang, Y., and Lu, Z. (2021). A Fast Point Clouds Registration Algorithm for Laser Scanners. Appl. Sci., 11.","DOI":"10.3390\/app11083426"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/10\/389\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:00:41Z","timestamp":1760144441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/10\/389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":27,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["a15100389"],"URL":"https:\/\/doi.org\/10.3390\/a15100389","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}