## 6 Conclusions and Future Research  
In this paper, we compared the current point cloud registration algorithms, which encompass as comprehensively as possible the various algorithms for point cloud alignment. Details of these classical methods and their applications to large-scale aircraft measurements are analyzed. Specifically, we first categorize the existing methods into hierarchical optimization, stochastic and probability distributions, and feature-based methods for analysis. Then, we presented the typical algorithms of each method, along with a summary of their strengths and weaknesses. Ultimately, the application of point cloud registration algorithms in large-size aircraft measurement is discussed.  
After many years of research, point cloud registration has achieved many remarkable results. However, more applications put forward greater challenges to point cloud registration as it gradually penetrates into the production life. The main challenges and future research directions are as follows:  
(1) Due to the constraint of view angle, realistic sensor-acquired point clouds typically produce partially overlapping point clouds, making the direct registration of these data challenging. Although some researchers have developed algorithms that can be aligned under partial overlap, the overlap rate is frequently constrained. Because of this, finding a fundamental solution to the partial overlap point cloud alignment problem is of great application value and promising area.  
(2) At present, although the point cloud registration technique combining deep learning and traditional methods has made significant progress, only simple objects can be solved for alignment now. For example, these approaches fail to achieve the desired results and still rely on the traditional algorithm, RANSAC [72], when dealing with complicated scenarios and large-scale point clouds. RANSAC algorithm, however, is stochastic and the number of iterations increases exponentially with outliers. Better outcomes were achieved by researchers who applied the RANSAC concept to neural networks. These findings demonstrate that the combination of neural networks and traditional techniques have greater potential. Generally speaking, traditional methods have the trait of transparency, while neural networks have strong fitting ability. As a result, how to combine the advantages of both is one of the future trends.  
(3) In different application scenarios, the algorithms encounter different obstacles, which demands the generality of point cloud registration algorithms. Nevertheless, it is challenging to propose a general algorithm, from the current research stage. For instance, in the case of aircraft, the aircraft's skin size is huge, the surface is smooth, and there are few curvature features. Consequently, serious misalignment will occur when employing feature-based methods in the registration process. Therefore, a more well-liked research hotspot is on the creation of focused, lightweight, and effective algorithms for certain application scenarios.  
**Author Contributions:** Conceptualization, supervision & funding acquisition, H.S.; Writing-original draft, review & editing, J.Q.; Writing--review & editing, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the Fundamental Research Funds for the Central Universities under Grant NS2020051, in part by the first batch of industry-university-research cooperative collaborative education projects of the Ministry of Education in 2021 under grant 202101042005, in part by the Experimental Technology Research and Development Project of Nanjing University of Aeronautics and Astronautics under grant SYJS202207Y.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.