An Automatic 3D Point Cloud Registration Method Based on Biological Vision

Jinbo Liu,Xiaoliang Sun,Pengyu Guo

doi:10.3390/app11104538

Abstract

When measuring surface deformation, because the overlap of point clouds before and after deformation is small and the accuracy of the initial value of point cloud registration cannot be guaranteed, traditional point cloud registration methods cannot be applied. In order to solve this problem, a complete solution is proposed, first, by fixing at least three cones to the target. Then, through cone vertices, initial values of the transformation matrix can be calculated. On the basis of this, the point cloud registration can be performed accurately through the iterative closest point (ICP) algorithm using the neighboring point clouds of cone vertices. To improve the automation of this solution, an accurate and automatic point cloud registration method based on biological vision is proposed. First, the three-dimensional (3D) coordinates of cone vertices are obtained through multi-view observation, feature detection, data fusion, and shape fitting. In shape fitting, a closed-form solution of cone vertices is derived on the basis of the quadratic form. Second, a random strategy is designed to calculate the initial values of the transformation matrix between two point clouds. Then, combined with ICP, point cloud registration is realized automatically and precisely. The simulation results showed that, when the intensity of Gaussian noise ranged from 0 to 1 mr (where mr denotes the average mesh resolution of the models), the rotation and translation errors of point cloud registration were less than 0.1° and 1 mr, respectively. Lastly, a camera-projector system to dynamically measure the surface deformation during ablation tests in an arc-heated wind tunnel was developed, and the experimental results showed that the measuring precision for surface deformation exceeded 0.05 mm when surface deformation was smaller than 4 mm.

Highlights

Near-space is a connected region of traditional aeronautics and space
This paper is organized as follows: In Section 2, we introduce the detection principles of cone vertices in detail; in Section 3, we derive an automatic registration algorithm for point clouds; in Section 4, we introduce the research methodology, including cone vertex detection, automatic registration, and surface deformation measurement; in Section 5, we present the research results, and in Sections 5.1 and 5.2 we present the accuracy and robustness of the cone vertex detection algorithm and automatic registration algorithm, respectively; in Section 5.3, we provide the results of3surface of 17 deformation measurement; in Section 6, we discuss the research results; and, in Section 7, we conclude with the contributions of this study and steps in research
In order to solve the problem of surface deformation measurement during ablation tests in an arc-heated wind tunnel, in this study, we proposed an automatic point cloud registration method

Summary

Introduction

Near-space is a connected region of traditional aeronautics and space. Near-space supersonic vehicles have great potential, but if flown for a long time in an aerothermal environment, the surface of vehicles can be deformed, which causes functional failure.deformation properties of materials in an aerothermal environment need to be urgently explored. Near-space is a connected region of traditional aeronautics and space. Near-space supersonic vehicles have great potential, but if flown for a long time in an aerothermal environment, the surface of vehicles can be deformed, which causes functional failure. There are many methods to reconstruct 3D shape data at different times during ablation tests, but the alignment of point clouds is difficult, because the overlap is too small. Point cloud registration involves calculating a rigid transformation matrix, consisting of a rotation matrix and a translation vector, to minimize the alignment error between two point clouds, and has been widely used for simultaneous localization and mapping (SLAM) [1,2,3], multi-view point cloud registration [4,5], object recognition [6,7], etc

Methods

Results

Discussion

Conclusion