Multi-level-point-set characteristic extraction method applicable to ground laser radar point cloud classification

Abstract

The invention relates to a multi-level-point-set characteristic extraction method applicable to ground laser radar point cloud classification. Based on point set characteristics, high-precision classification of four kinds of common ground features including pedestrians, trees, buildings and automobiles and the like in a scene is realized. Firstly, point sets are constructed and a point cloud is re-sampled into a point cloud of different scales and thus point sets which are different in size and provided with layered structures are formed through clustering and characteristics of each point in the point sets are obtained; next, an LDA (Latent Dirichlet Allocation ) method is adopted to synthesizing point-based characteristics of all points in each point set into shape characteristics of the point sets; and at last, based on the shape characteristics of the point set, an Adaboost classifier is adopted to train the point sets of different levels so as to obtain a classification result of the whole point cloud. The multi-level-point-set characteristic extraction method has a higher classification precision and has a classification precision, which is far higher than that of point-based characteristics, Bag-of-Word-based characteristics and characteristics based on probabilistic latent semantic analysis (PLSA), in aspect of pedestrians and vehicles.

Description

1. Technical field [0001] The invention relates to a method for extracting multi-level point set features suitable for ground laser radar point cloud classification, and belongs to the technical field of spatial information. 2. Background technology [0002] Only by effectively classifying and identifying ground lidar point clouds can the cognition of complex scenes be realized. The single-station ground lidar point cloud generally changes from sparse to dense with the distance from the scanner. If the scene range is large, the point density of near and far objects will differ by several times, and the point density will be uneven. As a result, the texture information of the same ground object in the same size window has a large difference. In addition to buildings and vegetation, there are also people and cars in urban scenes. These objects are often small and have different shapes, and are easily blocked by other objects, resulting in incomplete point clouds. Use this par...

AI Technical Summary

Problems solved by technology

Features based on single points are easily affected by noise. The existing point set features use features such as the average number of points and the average normal vector of the point set. In complex scenes, the stability of these features is poor

At present, there is still a lack of methods for effectively describing the characteristics of point sets. Based on this, the present invention studies a robust and highly distinguishable feature to express the target or point set, which can effectively describe the characteristics of each point. As well as the connection between points, it is well adapted to problems such as uneven density of ground lidar point clouds, noise, and missing data.

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