Point Cloud Registration Method Based on Improved TLBO for Landing Gear Components Measurement

Abstract

When using point cloud technology to measure the dimension and geometric error of aircraft landing gear components, the point cloud data obtained after scanning may have certain differences because of the sophistication and diversity of the components that make up the landing gear. However, when using traditional point cloud registration algorithms, if the initial pose between point clouds is poor, it can lead to significant errors in the final registration results or even registration failure. Furthermore, the significant difference in registration results between point clouds can affect the final measurement results. Adopting Teaching-Learning-Based Optimization (TLBO) to solve some optimization problems has unique advantages such as high accuracy and good stability. This study integrates TLBO with point cloud registration. To increase the probability of using TLBO for point cloud registration to search for the global optimal solution, adaptive learning weights are first introduced during the learner phase of the basic TLBO. Secondly, an additional tutoring phase has been designed based on the symmetry and unimodality of the normal distribution to improve the accuracy of the solution results. In order to evaluate the performance of the proposed algorithm, it was first used to solve the CEC2017 test function. The comparison results with other metaheuristics showed that the improved TLBO has excellent comprehensive performance. Then, registration experiments were conducted using the open point cloud dataset and the landing gear point cloud dataset, respectively. The registration results showed that the point cloud registration method proposed in this paper has strong competitiveness.