Point cloud pair constraint registration algorithm based on directed distance function

In order to solve the problem of high-precision registration of point cloud pairs with known correspondence under constrained conditions, a point cloud pairs registration method based on directed distance function is proposed. The point-to-point directed distance function is constructed by first order linear Taylor expansion, and LM algorithm is used to solve the least squares problem. An improved particle swarm optimization (PSO) algorithm is introduced to find the optimal normal vector between point pairs, so that the directed distance function iteratively approximates the sum of the minimum Euclidean distances between point pairs. The experimental results show that compared with SVD method, the proposed method is close to its accuracy under short distance variation, while the calculation accuracy is obviously improved under large distance complex variation.