Next Article in Journal
A Grid Connected Photovoltaic Inverter with Battery-Supercapacitor Hybrid Energy Storage
Previous Article in Journal
Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1862;

An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features

1,* , 1,2,* , 3
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China
Department of Automation, Tsinghua University, Beijing 100084, China
Shenzhen Graduate School, Tsinghua University, Shenzhen 518055, China
Authors to whom correspondence should be addressed.
Received: 28 April 2017 / Revised: 26 July 2017 / Accepted: 29 July 2017 / Published: 11 August 2017
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [5919 KB, uploaded 12 August 2017]   |  


The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process of accurate registration of 3D point cloud data. The algorithm requires a proper initial value and the approximate registration of two point clouds to prevent the algorithm from falling into local extremes, but in the actual point cloud matching process, it is difficult to ensure compliance with this requirement. In this paper, we proposed the ICP algorithm based on point cloud features (GF-ICP). This method uses the geometrical features of the point cloud to be registered, such as curvature, surface normal and point cloud density, to search for the correspondence relationships between two point clouds and introduces the geometric features into the error function to realize the accurate registration of two point clouds. The experimental results showed that the algorithm can improve the convergence speed and the interval of convergence without setting a proper initial value. View Full-Text
Keywords: ICP registration; geometric features; point clouds ICP registration; geometric features; point clouds

Figure 1

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

He, Y.; Liang, B.; Yang, J.; Li, S.; He, J. An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features. Sensors 2017, 17, 1862.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top