## 1 Introduction  
Aircraft assembly is a key part of aircraft manufacturing, and it directly determines the efficiency and quality of manufacturing [1, 2]. In the process of aircraft assembly, wing-body assembly is regarded as the most important assembly process because the wing box is the main load-bearing structure of the aircraft [3, 4, 5]. In the wing-body assembly process, the wing is fixed on the positioner and the fit of the outer wing and the central wing box is achieved by the adjustment of the wing position and attitude, and the assembly quality is ensured by the assembly gap between the wing and the fuselage [6]. Due to gravity, positioner load, time-varying temperature, and other factors, the composition docking surface profile of the assembly process is time-varying, and wing-body assembly can only be completed through the iterative process of profile measurement and pose adjustment [7]. In this process, the 3D point cloud data of the joint surface are often obtained by digital measurement instruments such as laser scanners [5, 8] and LIDAR [9, 10] and fed to the positional control system. The obtained point cloud data, however, are often huge and contain noisy data due to the huge volume of aircraft components and interference from other equipment, which reduces the analysis and processing speed of measurement data and increases the difficulty of data storage [8, 11]. The processing and fusion of point cloud data is the basic technology for obtaining high-quality information in the assembly process, which can effectively reduce the interference brought by the environment and improve the analysis efficiency, and ultimately help to improve the quality and efficiency of aircraft assembly. The pre-processing of point cloud data is the basis for subsequent point cloud alignment, gap evaluation and other aspects [12, 13, 14]. The purpose of pre-processing is to reduce the amount of data and effectively filter the noise while maintaining the key features of the components as much as possible [15, 16, 17]. For the pre-processing problem of point cloud data, the two main dimensions of point cloud resampling and denoising are carried out at this stage. [PERSON] [18] et al. used the moving least squares method to resample and smooth point cloud data for the problem of point cloud measurement errors and outliers, and the related method was applied to A320 surface defect detection. [PERSON] et al. [19] proposed a resampling strategy based on graph filter to address the difficulty of storing and processing large-scale point cloud data. [PERSON] et al. [20] improved the speed of the 3D scanning of hull surfaces by removing outliers through statistical filters and performing curvature-aware resampling by removing redundant information in low-curvature regions. [PERSON] et al. [21] used tensor voting theory to process point cloud data to infer hidden structure information from a 3D point cloud with strong noise and outliers and verified the effectiveness of the proposed method in aircraft skin seam detection. [PERSON] et al. [22] proposed a noise point removal method based on geometric feature constraints for the point cloud noise problem caused by occlusion, which achieves the effective removal of sparse and isolated outliers. However, the above methods are mainly for small-range point cloud data, and are less efficient and less adaptable for large-scale point cloud data. In addition, the point cloud data of aircraft assembly is more diverse and complex, and the traditional methods cannot achieve a better noise removal effect. This paper proposes a point cloud pre-processing method for the aircraft assembly process wing-body alignment problem.