针对目前散乱点云特征提取算法存在计算量大且不能规则化提取的问题,提出一种融合改进Canny算法的点云特征规则化提取算法。根据散乱点云的距离分辨率进行重采样,将点云进行规则栅格化;通过优化替代法对网格矩阵进行灰度值赋值,散乱点云被投影成二维影像;利用改进Canny算法从二维影像中进行特征规则化提取。对比实验结果表明:该方法噪声少、可操作性强,可以高效地对直线边界或复杂的曲线边界进行特征规则化提取。对点云与图像的配准以及后期三维重建等有重大作用。

A regularized feature extraction algorithm based on the improved Canny algorithm is proposed to address the problems related to applying a feature extraction algorithm to scattered point clouds, i.e., computational burden and lack of regularization. First, scattered point clouds are resampled according to different range resolutions, and the point clouds are rasterized regularly. Second, the gray value of the grid matrix is assigned by the optimized substitution method, and the scattered point clouds are projected into two-dimensional images. Finally, the improved Canny algorithm is used to extract feature regularization from the two-dimensional images. Comparative experimental results demonstrate that the proposed method yields less noise, has strong maneuverability, and can extract features from a straight line boundary or complex curve boundary efficiently. The proposed method will play an important role in the registration of point clouds and images, as well as three-dimensional reconstruction.