## 1 Introduction  
The typical three-dimensional data collecting approach, which is inefficient and time-consuming in complicated settings, obtains three-dimensional coordinates primarily through a single station, making detailed descriptions of complex structural planes and entities impossible. Additionally, there are certain issues with data collecting and post-processing hardware, such as laborious operation, high inaccuracy, and instability. In comparison with the commonly used single-point measurement approach, laser scanners may employ a significant number of three-dimensional coordinate data to swiftly measure and describe the object's surface, acquiring the target object's three-dimensional coordinate data set. Point clouds are another name for three-dimensional coordinate data. TLS technology has the advantages of fast speed, multiple data sampling rate, high data acquisition accuracy, low external impact, non-contact measurement, high degree of digitization, and the ability to be utilized in combination with GPS (global positioning system) and high-definition digital cameras. It can capture dangerous targets and environmental data more effectively, as well as gain more objective geographical information, resulting in more comprehensive information data collection. Once the point cloud data have been spliced, it may be possible to reconstruct a three-dimensional model for the next stage of analysis and investigation. Active acquisition equipment represented by laser scanning is maturing in terms of simplicity of operation, adaptability, intelligence, and efficiency. It can rapidly and accurately reconstruct the three-dimensional entity of the measured item by accurately capturing the spatial coordinates, color texture, reflection intensity, and other information of dense spots. Engineering measurements include forest structure assessments [1; 2; 3; 4; 5; 6; 7; 8; 9; 10], landslide surveillance [11; 12; 13; 14; 15; 16], and 3D model reconstruction [17; 18; 19; 20; 21; 22; 23; 24; 25], which play a critical role in scientific and engineering research. However, due to the effect of measurement environment, instruments and equipment, etc., the acquired point cloud in each frame only comprises a portion of the point cloud information about the measured object when carrying out the actual measurement. Therefore, to obtain the whole point cloud of the measured object, it is often essential to carry out multi-site and multi-angle measurements of the object and register the point cloud recorded from multiple angles to the same coordinate system. This paper aims to provide a comprehensive survey of the field of point cloud registration for laser scanners and discuss its application in large-scale aircraft measurement. At present, there have been many surveys about point cloud registration. [PERSON] et al. [26] provided an overview of ICP and its improved algorithm. [PERSON] et al. [27] concentrated on non-learning rough point cloud registration algorithms. [PERSON] et al. [28] classified point cloud registration techniques as fine or rough, although neither of them involved learning-based methods. Similarly, [PERSON] et al. [29] categorized point cloud registration methods into fine and rough registration methods, with some rough methods based on learning involved, although space was restricted. [PERSON] et al. [30] conducted a comprehensive survey of the point cloud registration algorithm and summarized the relationship between optimization-based and deep learning methods, plus the application across various domains about benchmark data sets in point cloud registration, to provide further research insight. The remainder of this review is organized as follows. We subdivide the existing point cloud registration methods into three categories in Sections 2-4, respectively: hierarchical optimization, stochastic and probability distribution, and feature-based methods for analysis. Section 2 introduces ICP and its variants along with graph matching-based algorithms.