Method for rapidly extracting indoor three-dimensional line segment structure based on point cloud data

Abstract

The invention provides a method for quickly extracting an indoor three-dimensional line segment structure based on point cloud data. The method comprises the steps of: measuring and collecting point cloud data of an indoor building, subjecting the point cloud data to projection, and forming two-dimensional projection point cloud data; extracting line segments in the two-dimensional projection point cloud data, calculating a spatial geometric equation of a vertical face through inversion by utilizing coordinates of end points of the line segments, and then acquiring a facade through segmentation; removing facade points from the two-dimensional projection point cloud data, performing down-sampling on the remaining points, acquiring a horizontal plane through segmentation, and combining the facade and the horizontal plane to obtain a final plane segmentation result; for each extracted plane in the plane segmentation result, fitting precise parameters of the plane, projecting points on theplane to the fitted fitting surface, and extracting edge points of each plane to obtain an edge point set; and setting the farthest distance from a point to a straight line, extracting three-dimensional line segments, and further performing line segment combination according to the parallelism, the colinearity and the coincidence between the line segments to obtain a final three-dimensional linesegment structure extraction result.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional laser scanning, and in particular relates to a method for quickly extracting an indoor three-dimensional line segment structure based on point cloud data. Background technique [0002] In recent years, 3D lidar technology has developed rapidly, and large-scale and high-density point cloud data are easy to obtain. At the same time, massive point clouds have brought great challenges to data processing and information extraction. Due to the unstructured, irregular, and non-uniform characteristics of raw point cloud data, it is necessary to abstract it concisely and meaningfully. As one of the most common features of the real environment, the line segment structure plays an important role in many fields such as 3D reconstruction, registration, positioning, calibration, road extraction, and object recognition. [0003] At present, point cloud 3D line segment structure extraction is still an ...

AI Technical Summary

Problems solved by technology

At present, the method based on the plane is better, that is, first segment the plane from the point cloud, and then extract the contour points and three-dimensional line segments from the plane, but the traditional plane segmentation algorithm has problems such as low efficiency, over-segmentation, under-segmentation, high Memory consumption, threshold setting depends on experience, etc.

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