# An Automatic 3D Point Cloud Registration Method Based on Biological Vision
###### 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\({}^{\circ}\) 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. point cloud registration; biological vision; automatic registration; cone vertices; deformation measurement +
Footnote †: journal: Applied  
An Automatic 3D Point Cloud Registration Method Based on Biological Vision  
[PERSON]; [PERSON]. registration [8]. It was proposed by [PERSON], in 1992, and has been used to solve the registration problem of free-form surfaces. The basic idea is to search a corresponding closest point in a test point cloud for each point in a reference point cloud. According to the set consisting of all the closest points, a transformation matrix is calculated between two point clouds, resulting in a registration error. If the registration error cannot satisfy the stopping criterion, the transformed test point cloud is taken as a new test point cloud, and the above steps are repeated until the stopping criterion is satisfied. In the classical ICP algorithm, the registration error is presented in two forms, i.e., point-to-point and point-to-plane. In general, the ICP algorithm based on the point-to-plane registration error converges at a faster rate [9]. Using the classical ICP algorithm as a foundation, there have been many variants proposed by researchers [10; 11; 12; 13; 14]. The key step of these methods involves searching the set of closest points. However, in a real scenario of surface deformation, it is very difficult to accurately determine the closest points without any prior information. Moreover, these methods are very sensitive to the initial values of the transformation matrix. If the quality of initial values is not good enough, these methods easily suffer from local minima. Another way to solve the point cloud registration problem is by using probabilistic methods. The core idea is to transform the registration problem of point clouds into an optimization problem of probability model parameters [15; 16; 17; 18; 19; 20]. As compared with ICP and its variants, these methods perform more robustly with respect to noise and outliers, but their computational complexity is high.