Review on Algorithm for Fusion of Oblique Data and Radar Point Cloud

With the development of “Digital Earth,” “Reality-Based 3D China,” and “Smart Cities,” technologies such as UAV aerial photography, photogrammetry, LiDAR, oblique photogrammetry, and SLAM are increasingly utilized for constructing reality-based 3D models. However, each individual technology has its limitations in 3D reconstruction, especially in the fine modeling of buildings, such as occlusion in texture mapping and low model accuracy. While oblique photogrammetry captures multi-angle images of terrains, it encounters challenges when acquiring images in concealed locations, resulting in structural deformations and surface artifacts, leading to insufficient model precision and poor elevation accuracy. LiDAR point clouds complement the geometric structure in the blind areas of oblique photogrammetry, resulting in smoother ground surfaces and sharper edges and lines at the base of buildings. Integrating vehicle-mounted LiDAR point clouds with oblique photogrammetry effectively compensates for the limitations of using a single data source for 3D model creation and improves model accuracy. In 3D reconstruction, the fusion of oblique photogrammetry and LiDAR data is crucial. The classical Iterative Closest Point (ICP) algorithm, widely used in point cloud registration, iteratively finds the closest point pairs between two point sets to calculate the transformation matrix, converging to a certain threshold. However, ICP requires a high initial position accuracy of point clouds, and its simple selection of corresponding points based on Euclidean distance may lead to mismatches, impacting the registration precision. As a result, numerous scholars have made improvements and research on this algorithm.