## 5 Application in Large-Scale Aircraft Measurement  
Today, as measurement technology picks up steam, it has become conceivable to scan the entire surface of an aircraft with a 3D laser scanner without traditional contact measurements. This technology is appropriate for aircraft measuring situations due to its non-contact, wide measurement range, and active illumination offered by the scanner. It can instantly deliver millions of high-quality data points to depict the aircraft's surface. Due to occlusions and equipment restrictions, it is sometimes impractical to obtain the whole surface of huge objects like airplanes at a single location. Multi-station scanning is required when scanning the entire aircraft surface. As a result, a fundamental and crucial component of aircraft detection is learning how to register point clouds in several coordinate systems into the global coordinate system. To tackle the issue of the poor efficiency of extracting boundary features from thin-walled structural sections of aircraft, [PERSON] et al. [141] suggested a line-by-line processing algorithm of the scan line point cloud based on its distribution characteristics. The boundary feature line is fitted once the local measurement points have been collected by neighborhood search and projected onto the scanning plane. This algorithm can avert several time-consuming and challenging calculations such as point cloud partition surface fitting, thereby improving the processing efficiency of large-scale point clouds. In addition to analyzing point cloud and CAD model registration approaches, [PERSON] [142] also analyzed point cloud sampling, streamlining, denoising feature extraction, and other techniques. The one-time alignment of large-size aeronautical structural parts is achieved through human-machine interaction. The efficiency of point cloud matching and alignment precision may be considerably increased by area mapping to find corresponding points and raising the weight factor of assembly characteristics compared with the traditional ICP. [PERSON] et al. [143] evaluated the coordinate systems between each measurement system and the assembly frame for the measurement of major components in aircraft assemblies. They also accomplished this by improving the alignment of ERS (enhanced reference system) points. [PERSON] [144] introduced a unique ICP algorithm for aircraft parts point cloud by fusing similar feature matching with the weighted Euclidean distance threshold method, which has a superior registration effect for small-sized parts. The alignment effect is not ideal since the approach cannot extract precise comparable characteristics for the registration process owing to the relatively smooth surface of an aircraft and the enormous size of the point cloud. [PERSON] et al. [145] adopted a laser scanner with multiple scanning stations to capture genuine point clouds. In addition, they provided a generic framework for aircraft point cloud registration to concurrently align multiple point cloud stratifications with the global coordinate system. The method estimates the overlapping area based on the density-invariant area method and adopts the hierarchical optimization registration method to realize the multi-view registration of aircraft point clouds, allowing the whole geometry of the aircraft to be precisely acquired. To address the problem of the low accuracy of aircraft point cloud registration, [PERSON] and [PERSON] [146] designed and implemented an accurate multi-view registration algorithm to improve the traditional hierarchical registration method by eliminating the effect of density variation in large-scale point clouds, enabling the segmented point clouds precisely registered and significantly enhancing the alignment accuracy. As a result of gentle, smooth, and few features on the surface of aircraft skin, [PERSON] et al. [147] suggested a contour constraint-based alignment technique for the skin point cloud in response to issues with misalignment and local minima when utilizing the ICP algorithm for number pattern matching.