Roof model reconstruction affects the quality of building complete model reconstruction, and the segmentation quality of roof point cloud is of great significance for roof model reconstruction. Aiming at the problems of wrong segmentation and over segmentation in the traditional RANSAC algorithm, this paper proposes an improved RANSAC algorithm to redistribute the point cloud, considering the location information of the point cloud. The algorithm eliminates the non planar points temporarily, and selects three points from the planar points set as the initial samples in the way of R radius neighborhood to fit them. The distance between the remaining points in the neighborhood and the fitting plane is calculated, and the neighborhood meeting the threshold requirements is classified as an effective neighborhood, three points with the minimum standard deviation are selected as the initial model, RANSAC algorithm is used to segment the roof point cloud. Aiming at the misclassification phenomenon in segmentation results, the distance between misclassification points and patches is calculated by k-nearest neighbor algorithm, and then the misclassification points are reclassified, at the same time, the angle θ and the distance d between patches are considered to merge the over segmented patches, the Euclidean distance based clustering segmentation algorithm is used to analyze the connectivity of the merged patches. By using the distance from a point to a plane and the consistency of the normal vectors between the point and the plane, the non planar points are redistributed. In order to verify the effectiveness of the algorithm, three independent roofs of complex buildings in Helsinki area of Finland and six roofs of buildings in a residential area of Shanghai are selected as experimental data. In the first group of experiments data, the average accuracy of the segmentation of roof patch is 92.17%, and the highest accuracy is 93.18%. In the second group of experiments data, the average accuracy of the segmentation of the roof patch is 87.82%, and the highest accuracy is 94.44%. The average standard deviation of the distance between the points on all the segmentation patches and the corresponding best fitting plane is 0.030 m. According to the above two groups of experiments data, 78% of the buildings have no over segmentation, and the average accuracy is 90%. The experimental results show that the algorithm has a high accuracy in extracting the roof plane slice, which can suppress the over segmentation and has a good anti noise ability. © 2021, Science Press. All right reserved.
