基于二次误差的点云配准算法.

This paper proposed a feature descriptor based on quadratic error, which had rotation invariance. It obtained two feature descriptors by extracting the quadratic error of the point and the quadratic error of the neighboring point. It emerged point cloud registration algorithms based on the Gaussian mixture model in an endless stream. The main reason was that probabilistic models had better robustness in terms of noise and outliers. However, this type of method did not perform well for larger-scale rotations. This paper used the quadratic error feature descriptor as a local feature of the Gaussian mixture model to optimize the registration effect in the larger rotation of the Gaussian mixture model, and proposed a dual-feature-based registration strategy to optimize the defects of a single feature. Through experiments, compared with the robust ICP and popular feature-based registration algorithm in terms of registration efficiency and precision, the efficiency of the algorithm is 3 - 4 times that of the robust ICP. In large-scale rotation, the proposed algorithm has good robustness and is superior to the most popular algorithms. [ABSTRACT FROM AUTHOR]