Next Article in Journal
A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain
Previous Article in Journal
Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessFeature PaperEditor’s ChoiceArticle

Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments

Univ Lyon, LIRIS, UMR 5205 CNRS, Université Claude Bernard Lyon 1, 43 bd du 11 Novembre 1918, 69622 Villeurbanne CEDEX, France
Institut Pascal, UMR 6602, Université Clermont Auvergne, CNRS, SIGMA Clermont,F-63000 Clermont-Ferrand, France
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(10), 1014;
Received: 4 August 2017 / Revised: 22 September 2017 / Accepted: 26 September 2017 / Published: 30 September 2017
PDF [9010 KB, uploaded 1 October 2017]


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. View Full-Text
Keywords: 3D LiDAR; registration; Gaussian sphere; point clouds; structured scenes 3D LiDAR; registration; Gaussian sphere; point clouds; structured scenes

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Sanchez, J.; Denis, F.; Checchin, P.; Dupont, F.; Trassoudaine, L. Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments. Remote Sens. 2017, 9, 1014.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top