基于二次误差的点云配准算法

Point cloud registration algorithm based on quadric error

提出了一种基于二次误差的特征描述子,该特征描述子具有旋转不变性。通过提取点的二次误差和邻域点二次误差得到两种特征描述子。基于高斯混合模型的点云配准算法层出不穷,主要原因是概率模型在噪声和离群值方面具有更好的鲁棒性,然而该类方法对于尺度较大的旋转表现并不好,为此将二次误差特征描述子作为高斯混合模型的局部特征优化了高斯混合模型较大旋转中的配准效果,并提出基于双特征的配准策略优化了单一特征的缺陷。通过实验与鲁棒的ICP(iterative closest point)以及流行的基于特征的配准算法在配准效率和配准精度方面进行对比,效率是鲁棒性ICP的3~4倍。在大尺度的旋转中提出的算法具有良好的鲁棒性并且优于大多数流行的算法。

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.