A Method for Automatic Cable Extraction and Reconstruction Based on 3D Point Cloud Data

Abstract

The invention discloses a cable automatic extraction reconstruction method based on three dimensional point cloud data; the method comprises the following steps: S1, obtaining a three dimensional point cloud data set P, and segmenting mesh for the whole data set P; S2, using a vertical interval filtering algorithm and a density-based filtering algorithm to filter ground and plant point sets; S3, using probability Hough transform to extract the cable; S4, using a rectangular buffer area connection algorithm to connect line segments; S5, re-using the filtering algorithm to reduce the threshold for filtering, and setting the filtered plant point set as the danger point; S6, using the integral least square algorithm to fit a x-y plane straight line with a x-z plane suspension curve. The methodis good in applicability, stability and robustness.

Description

technical field [0001] The invention relates to the technical field of three-dimensional point cloud data processing, in particular to an automatic cable extraction and reconstruction method based on three-dimensional point cloud data. Background technique [0002] The cable network is the infrastructure of the country, it requires precise inspection and maintenance, otherwise it will suffer heavy losses in the event of an accident. The traditional cable detection method is mainly a combination of ground and air detection. The ground detection method is to assign patrol personnel to manually observe the cable network to check whether there are potential accidents in the cable and surrounding trees. However, this method is limited by factors such as uneven distribution of personnel and steep terrain of the cable network. The aerial detection method mainly uses small aircraft, helicopters, drones, etc. to detect cables, and can obtain aerial color images and infrared images. ...

AI Technical Summary

Problems solved by technology

[0004] However, the current research on airborne lidar data processing is mainly focused on each step in the process. For example, the 8-neighborhood selection algorithm is often used for filtering to filter out the ground and tree point sets. Surrounded by , the echo points in the 8-neighborhood of the cable network include not only the ground but also the bush points, resulting in filtering out too many points or even filtering out the cable line points; while the least squares is commonly used in the fitting reconstruction of the cable Multiply linear fitting, but this method is computationally complex, which is not conducive to fast calculation

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