An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features
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,...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-08-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/17/8/1862 |
id |
doaj-c9444236c93b4d5e81a0fc37d2d59126 |
---|---|
record_format |
Article |
spelling |
doaj-c9444236c93b4d5e81a0fc37d2d591262020-11-24T22:10:56ZengMDPI AGSensors1424-82202017-08-01178186210.3390/s17081862s17081862An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric FeaturesYing He0Bin Liang1Jun Yang2Shunzhi Li3Jin He4Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, ChinaShenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, ChinaShenzhen Graduate School, Tsinghua University, Shenzhen 518055, ChinaShenzhen Graduate School, Tsinghua University, Shenzhen 518055, ChinaDepartment of Automation, Tsinghua University, Beijing 100084, ChinaThe 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.https://www.mdpi.com/1424-8220/17/8/1862ICP registrationgeometric featurespoint clouds |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ying He Bin Liang Jun Yang Shunzhi Li Jin He |
spellingShingle |
Ying He Bin Liang Jun Yang Shunzhi Li Jin He An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features Sensors ICP registration geometric features point clouds |
author_facet |
Ying He Bin Liang Jun Yang Shunzhi Li Jin He |
author_sort |
Ying He |
title |
An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features |
title_short |
An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features |
title_full |
An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features |
title_fullStr |
An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features |
title_full_unstemmed |
An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features |
title_sort |
iterative closest points algorithm for registration of 3d laser scanner point clouds with geometric features |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2017-08-01 |
description |
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. |
topic |
ICP registration geometric features point clouds |
url |
https://www.mdpi.com/1424-8220/17/8/1862 |
work_keys_str_mv |
AT yinghe aniterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT binliang aniterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT junyang aniterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT shunzhili aniterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT jinhe aniterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT yinghe iterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT binliang iterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT junyang iterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT shunzhili iterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures AT jinhe iterativeclosestpointsalgorithmforregistrationof3dlaserscannerpointcloudswithgeometricfeatures |
_version_ |
1725806358627876864 |