Line laser point cloud segmentation based on the combination of RANSAC and region growing
| Autor: | Wei Sun, Tianyuan Xiang, Henan Yuan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud Process (computing) Image processing 02 engineering and technology RANSAC Curvature 020901 industrial engineering & automation Region growing Computer Science::Computer Vision and Pattern Recognition Line (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence business Astrophysics::Galaxy Astrophysics |
| Zdroj: | 2020 39th Chinese Control Conference (CCC). |
| DOI: | 10.23919/ccc50068.2020.9188506 |
| Popis: | RANdom SAmpling Consensus (RANSAC) and region growing algorithms are widely used in image processing and point cloud segmentation, but the RANSAC algorithm used for point cloud segmentation will cause insufficient segmentation. The region growing algorithm can divide point cloud data into points based on the curvature and normal characteristics of the point cloud. Multiple clusters are easy to be over-segmented. To solve this problem, this paper proposes to use the RANSAC algorithm to perform coarse segmentation to segment the point cloud data into a foreground point cloud with more geometric features and a background point cloud that is only a plane. Then use the region growing algorithm. The foreground point cloud is finely segmented. Besides, the curvature characteristics of the region growing process are used to optimize the plane extraction of the RANSAC algorithm. The experimental results show that this method can reduce over-segmentation to a certain extent and significantly improve the speed of the algorithm. |
| Databáze: | OpenAIRE |
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