{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T17:45:21Z","timestamp":1762623921271,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,30]],"date-time":"2017-09-30T00:00:00Z","timestamp":1506729600000},"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>Acquiring 3D data with LiDAR systems involves scanning multiple scenes from different points of view. In actual systems, the ICP algorithm (Iterative Closest Point) is commonly used to register the acquired point clouds together to form a unique one. However, this method faces local minima issues and often needs a coarse initial alignment to converge to the optimum. This paper develops a new method for registration adapted to indoor environments and based on structure priors of such scenes. Our method works without odometric data or physical targets. The rotation and translation of the rigid transformation are computed separately, using, respectively, the Gaussian image of the point clouds and a correlation of histograms. To evaluate our algorithm on challenging registration cases, two datasets were acquired and are available for comparison with other methods online. The evaluation of our algorithm on four datasets against six existing methods shows that the proposed method is more robust against sampling and scene complexity. Moreover, the time performances enable a real-time implementation.<\/jats:p>","DOI":"10.3390\/rs9101014","type":"journal-article","created":{"date-parts":[[2017,10,2]],"date-time":"2017-10-02T13:10:05Z","timestamp":1506949805000},"page":"1014","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments"],"prefix":"10.3390","volume":"9","author":[{"given":"Julia","family":"Sanchez","sequence":"first","affiliation":[{"name":"Univ Lyon, LIRIS, UMR 5205 CNRS, Universit\u00e9 Claude Bernard Lyon 1, 43 bd du 11 Novembre 1918, 69622 Villeurbanne CEDEX, France"}]},{"given":"Florence","family":"Denis","sequence":"additional","affiliation":[{"name":"Univ Lyon, LIRIS, UMR 5205 CNRS, Universit\u00e9 Claude Bernard Lyon 1, 43 bd du 11 Novembre 1918, 69622 Villeurbanne CEDEX, France"}]},{"given":"Paul","family":"Checchin","sequence":"additional","affiliation":[{"name":"Institut Pascal, UMR 6602, Universit\u00e9 Clermont Auvergne, CNRS, SIGMA Clermont,F-63000 Clermont-Ferrand, France"}]},{"given":"Florent","family":"Dupont","sequence":"additional","affiliation":[{"name":"Univ Lyon, LIRIS, UMR 5205 CNRS, Universit\u00e9 Claude Bernard Lyon 1, 43 bd du 11 Novembre 1918, 69622 Villeurbanne CEDEX, France"}]},{"given":"Laurent","family":"Trassoudaine","sequence":"additional","affiliation":[{"name":"Institut Pascal, UMR 6602, Universit\u00e9 Clermont Auvergne, CNRS, SIGMA Clermont,F-63000 Clermont-Ferrand, France"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.cag.2015.07.008","article-title":"Automatic reconstruction of parametric building models from indoor point clouds","volume":"54","author":"Ochman","year":"2016","journal-title":"Comput. 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