Point Cloud Pose Alignment Based on Improved Nearest Point Iteration

The ICP alignment algorithm requires high initial position of the point cloud to be aligned, and the alignment process is time-consuming, so a point cloud positional alignment algorithm based on improved nearest point iteration is proposed. For the error problem of mismatch removal, the adaptive threshold of mismatch removal is proposed, and its threshold is determined by the mean and standard deviation of the point distance set. To address the problem of underutilization of point cloud features by the cost function, a cost function based on the covariance matrix of the point distribution is established using the processing idea of probability theory. This cost function can also avoid nonlinear computation. The experiments show that this algorithm avoids the ICP alignment algorithm from falling into local optimum due to the initial position pose, and improves the alignment efficiency and accuracy at the same time.