BASED ON GPU FOR STRIP ADJUSTMENT ALGORITHM OF LIDAR DATA
Keywords: GPU, strip adjustment, Iterative Closest Point, Octree, Hashing function
Abstract. In order to solve the problem that the source of LiDAR data error needs to be adjusted and the data volume is large, the adjustment speed between the voyages is slow and cannot be automatically adjusted. Based on the iterative nearest point (ICP) algorithm, this paper proposes an improved iterative closest point (ICP) algorithm based on GPU parallel octree. The algorithm quickly constructs the octree of LiDAR nautical belt data in the GPU, uses the octree to quickly find the overlapping area of the nautical band, and then uses the ICP algorithm in the overlapping area to solve the adjustment parameters R and T quickly. Then the entire flight belt is quickly adjusted. Experiments with example data show that this method can quickly and automatically adjustment a large number of LiDAR data, and the adjustment precision can meet the precision requirements of the production.