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

Ying He,Bin Liang,Jun Yang,Shunzhi Li,Jin He

doi:10.3390/s17081862

Abstract

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.

Highlights

The 3D point cloud of the object surface can be obtained by optical equipment such as laser scanners, which can provide the basis for the establishment of the 3D model of the object
In order to evaluate the accuracy of the Iterative Closest Points (ICP) algorithm based on point cloud features, this section compares the algorithm with the performance of the main variants of ICP algorithms (ICP algorithm based on quaternion and ICP algorithm based on a kd-tree)
The point cloud data is down-sampled by MATLAB (After sampling, the bunny is 3951 and dragon is 4377), and the point cloud position is generated randomly, the rotation matrix and translation vector are respectively: In order to evaluate the accuracy of the ICP algorithm based on point cloud features, this section compares the algorithm with the performance of the main variants of ICP algorithms

Summary

Introduction

The 3D point cloud of the object surface can be obtained by optical equipment such as laser scanners, which can provide the basis for the establishment of the 3D model of the object. Points (ICP) algorithm proposed by Besl [2]. In this method, the transformation parameters of two point sets are calculated through the relationship between the corresponding matching points of two point sets to satisfy the given convergence precision, and the translation and rotation parameters between the two points are obtained to complete the registration process. There are some problems with the traditional ICP algorithm [3], where the initial value of the iteration should be determined when the first step of the ICP algorithm is performed. The selected initial value will have major effect on the final registration result. If the selection of the initial value is not Sensors 2017, 17, 1862; doi:10.3390/s17081862 www.mdpi.com/journal/sensors

Methods

Results

Conclusion