Abstract
In the fields of wind tunnel measurement and aerospace, the real-time pose information of aircraft is an important index. In this paper, we propose a large-scale aircraft pose estimation system, in which depth cameras are used to scan the entire aircraft model in multiple directions. Using a principal component analysis (PCA) featuring vectors as the target coordinate system through a coordinate transformation matrix for the point cloud calibration of aircraft, we merge the complete aircraft model with the point cloud. An intrinsic shape signature (ISS) key point extraction and a signature of histograms of orientations (SHOT) feature description are used to form feature descriptors. The scale of the point clouds is reduced, and coarse registration of the point clouds is performed by feature matching and random sample consensus (RANSAC) mismatching. The robustness of the algorithm is improved, and the initial pose estimation is achieved for the precise registration of point clouds. The experimental results demonstrate that the proposed system can achieve an angle measurement accuracy of 0.05°.
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