Local sampling-based multi-geometrical characteristic point cloud data splitting method

Abstract

The invention discloses a local sampling-based multi-geometrical characteristic point cloud data splitting method. The method includes the following steps: a three-dimensional rule grid is utilized to divide all acquired point cloud data into a plurality of first grid units; the process of extracting each geometrical characteristic includes the following steps: a sampling point is randomly chosen from the first current data set, the current first grid unit with the sampling point is determined, the sampling point and the other points in the current first grid unit are utilized to construct Alpha candidate geometrical models, an optimal model is determined from the Alpha candidate geometrical models, a consistent set of the optimal model is calculated in the first current data set, and according to multiple geometrical characteristics, all the point cloud data are split into a plurality of subsets. From local sampling, the method constructs candidate geometrical models from a first grid unit, and determines an optimal model from the candidate geometrical models, so that a geometrical characteristic is extracted, thus the problem of fitting models not existing in reality is prevented; and the method is more efficient.

Description

Technical field [0001] The invention relates to a method for segmenting point cloud data, in particular to a method for segmenting multi-geometric feature point cloud data based on local sampling, and the method is more suitable for processing massive point cloud data. Background technique [0002] Laser point cloud data segmentation is a process of segmenting point cloud data into several disjoint subsets according to certain attributes or rules. At present, laser point cloud data segmentation mainly uses geometric information (curvature, normal direction, Gaussian sphere, etc.) or spectral information (multiple combined geometric information) of the point cloud data. Methods based on geometric information mainly include edge-based segmentation, surface-based segmentation, and other methods. The edge-based segmentation method is to detect the sudden change boundary according to certain attributes or rules, and segment the data volume according to the sudden change boundary; the...

AI Technical Summary

Problems solved by technology

However, the traditional unoptimized RANSAC method adopts a global sampling strategy, that is, selects sampling points from all point cloud data to construct a geometric model, which is prone to the problem of "fitting a model that does not exist in the real world". For example, when three sampling points are selected from all the point cloud data inside a building to fit a plane, these three sampling points may come from the roof, wall and floor respectively, so that the obtained plane is in The real world does not exist at all, and the results obtained are far from the reality, which affects the computational efficiency; at the same time, the RANSAC method can only determine one geometric feature from the point cloud data in one calculation, and the real artificial objects are mostly in a variety of Therefore, how to effectively extract multi-geometric primitive features from massive point cloud data is of great significance in practical applications.

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