A Review of Point Cloud Registration Algorithms for Laser Scanners: Applications in Large-Scale Aircraft Measurement

Abstract

As 3D acquisition equipment picks up steam, point cloud registration has been applied in ever-increasing fields. This paper provides an exhaustive survey of the field of point cloud registration for laser scanners and examines its application in large-scale aircraft measurement. We first researched the existing representative point cloud registration algorithms, such as hierarchical optimization, stochastic and probability distribution, and feature-based methods, for analysis. These methods encompass as many point cloud registration algorithms as possible; typical algorithms of each method are suggested respectively, and their strengths and weaknesses are compared. Lastly, the application of point cloud registration algorithms in large-scale aircraft measurement is introduced. We discovered that despite the significant progress of point cloud registration combining deep learning and traditional methods, it is still difficult to meet realistic needs, and the main challenges are in the direction of robustness and generalization. Furthermore, it is impossible to extract accurate and comparable features for alignment from large-scale aircraft surfaces due to their relative smoothness, lack of obvious features, and abundance of point clouds. It is necessary to develop lightweight and effective dedicated algorithms for particular application scenarios. As a result, with the development of point cloud registration technology and the deepening into the aerospace field, the particularity of the aircraft shape and structure poses higher challenges to point cloud registration technology.