Point cloud segmentation algorithm based on feature fusion used for understory environments

Aimed at the complexity of geometric features of understory environments and the deficiency of edge detection based method, region growing based method and clustering feature based method, we propose a new point cloud segmentation algorithm based on feature fusion. The 3D data set acquired from Beijing Forestry University using a FARO laser scanner consists of 1166302 points after removing outliers and filtering. The data set has four targets, i.e., tree, ground, stone and person. Point cloud segmentation can be achieved via fusing normal vector and laser reflection intensity of each point. The laser reflection intensity values can be obtained from point cloud data set directly, and normal vector should be calculated based on the Plane PCA algorithm. Also, it is necessary to create kd-tree data structure and perform k-NN search during the calculation of normal vector. Segmentation is realized after fusing the advantages of normal vector and laser reflection intensity and calculating synthetical difference degree between query points and neighborhood points. Comparing the segmentation results from point cloud segmentation algorithms based on feature fusion, normal vector and laser reflection intensity, the method based on feature fusion overcomes the problem of data deficiency that the other two methods suffer.