Citation: Wang, B.; Zhou, J.;
Huang, Y.; Wang, Y.; Huang, B.
Improved Registration Algorithm
Based on Double Threshold Feature
Extraction and Distance Disparity
Matrix. Sensors 2022, 22, 6525.
https://doi.org/10.3390/s22176525
Academic Editors: Fang Cheng,
Qian Wang, Tegoeh Tjahjowidodo
and Ziran Chen
Received: 11 July 2022
Accepted: 24 August 2022
Published: 30 August 2022
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Article
Improved Registration Algorithm Based on Double Threshold
Feature Extraction and Distance Disparity Matrix
Biao Wang, Jie Zhou, Yan Huang, Yonghong Wang and Bin Huang *
School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,
Hefei 230009, China
* Correspondence: hbld@hfut.edu.cn
Abstract:
Entire surface point clouds in complex objects cannot be captured in a single direction by
using noncontact measurement methods, such as machine vision; therefore, different direction point
clouds should be obtained and registered. However, high efficiency and precision are crucial for
registration methods when dealing with huge number of point clouds. To solve this problem, an
improved registration algorithm based on double threshold feature extraction and distance disparity
matrix (DDM) is proposed in this study. Firstly, feature points are extracted with double thresholds
using normal vectors and curvature to reduce the number of points. Secondly, a fast point feature
histogram is established to describe the feature points and obtain the initial corresponding point
pairs. Thirdly, obviously wrong corresponding point pairs are eliminated as much as possible by
analysing the Euclidean invariant features of rigid body transformation combined with the DDM
algorithm. Finally, the sample consensus initial alignment and the iterative closest point algorithms
are used to complete the registration. Experimental results show that the proposed algorithm can
quickly process large data point clouds and achieve efficient and precise matching of target objects.
It can be used to improve the efficiency and precision of registration in distributed or mobile 3D
measurement systems.
Keywords:
point cloud registration; machine vision; feature extraction; double threshold; distance
disparity matrix; ICP algorithm
1. Introduction
With the rapid development of machine vision in recent years, vision technology
based on 3D point clouds has been widely used in the fields of industrial design, reverse
engineering, surface defect detection, and virtual reality. Compared with traditional 2D
images, 3D data provide richer information [
1
]. As a special information format that
contains complete 3D spatial data, 3D point cloud data have elicited extensive attention [
2
].
At present, methods for collecting 3D data include the time-of-flight [
3
], stereo vision [
4
],
laser scanning [
5
], and structured light [
6
] methods. Limited by the scanning angle of
the device and the shape of the object, the complete 3D information of an object must be
collected from multiple views, and point clouds must be registered into a complete model.
Point cloud registration is a key step in capturing the complete shape of 3D objects.
The purpose of point cloud registration is to find a 3D rigid body transformation,
such that the point cloud of the same object from different perspectives can be transformed
into the same coordinate system for rapid and accurate matching and splicing. Splicing
accuracy directly affects the accuracy of model reconstruction [
7
]. The same-source reg-
istration can be divided into optimization-based registration methods, feature-learning
methods, and end-to-end learning registration [
8
]. The deep learning-based methods do
not require iteration, but large training data is needed [
9
–
11
]. Besides, the registration
results are sensitive to noise. Optimization-based registration is to use optimization strate-
gies to estimate the transformation matrix without training data. The most widely used
Sensors 2022, 22, 6525. https://doi.org/10.3390/s22176525 https://www.mdpi.com/journal/sensors
