@article{CAI2025, 
author = {Shuyu CAI and Fengwei HAO and Tao SHI},
title = {Partition based on features of neighborhood points and corresponding point cloud registration of aero-engine damaged blade},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {3},
pages = {784-794},
keywords = {point cloud registration, feature extraction, iterative closest point, damage detection, compressor blade},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0081},
doi = {10.13700/j.bh.1001-5965.2023.0081},
abstract = {To satisfy the requirements on accuracy and efficiency of point cloud registration of damaged compressor blades a algorithm for partition based on features of neighborhood points and the corresponding accurate point cloud registration of aero-engine damaged blades was proposed. First of all, based on the covariance matrix, a multi-step partition model was employed to define the method to divide feature sub-blocks, and thus obtain effective feature regions. Secondly, a stable n-dimensional feature vector was constructed in accordance with the local curvature, the maximum distance between points, and the angle property of the maximum normal vector; then, by introducing the iterative closest point theory, the minimum Euclidean distance between the corresponding points and that from the point to the surface between the corresponding blocks were established. The accurate position correction of the two models was realized. Finally, the unit quaternion algorithm was used to complete the accurate point cloud registration of damaged blades. Experimental results show that the proposed algorithm can achieve point cloud registration on the surface of the point cloud model of damaged compressor blades, significantly improving the efficiency and accuracy of registration. Moreover, the advantages and robustness of the unit quaternion algorithm are verified through the point cloud database of multiple groups of aero-engine damaged blades.}
}