Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

LiDAR point cloud power line classification method based on normal random sampling distribution

A random sampling and laser radar technology, applied in the field of data processing, to achieve the effect of improving classification efficiency

Active Publication Date: 2021-12-10
HUNAN UNIV OF SCI & TECH
View PDF14 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] As a novel and efficient means of space detection, lidar technology can quickly acquire a large amount of point cloud data with precise three-dimensional spatial coordinates of the target scene in a short period of time. However, compared with the progress made in hardware performance and indicators of lidar systems , the software processing of LiDAR point cloud data is still in its infancy. Faced with the massive point cloud data acquired by the LiDAR hardware system, how to effectively use it is a major problem in the field of LiDAR point cloud data processing. question

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • LiDAR point cloud power line classification method based on normal random sampling distribution
  • LiDAR point cloud power line classification method based on normal random sampling distribution
  • LiDAR point cloud power line classification method based on normal random sampling distribution

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] Such as figure 1 As shown, a method for classifying lidar point cloud power lines based on normal random sampling distribution includes the following steps:

[0049] (1) Preprocess the original point cloud data, establish a digital terrain model, and use elevation filtering to roughly extract power line candidate points. The concrete steps of step (1) are:

[0050] 1-1) Based on the original point cloud data and the filtering mechanism of conventional and traditional significant non-power line points (noise points, false and missing points, etc.), the point cloud data is preprocessed;

[0051] 1-2) Describe the scene according to the quality of the original point cloud data, and design the ground seed point spacing of 0.5 meters;

[0052] 1-3) Process the point cloud data into blocks to obtain several small areas, and select a ground seed point ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a laser radar point cloud power line classification method based on normal random sampling distribution. Extraction; (2) For the roughly extracted power line candidate point data, based on the normal distribution transformation algorithm, the further optimized extraction of power line candidate points in three-dimensional space is realized; (3) For the optimized power line candidate point data, random sampling is adopted Based on the principle of linear algorithm, the precise extraction of power line points can be realized in two-dimensional space. The method of the present invention can realize power line classification in laser radar point cloud data in various complex environments such as urban forest areas, and provide accurate power line extraction results, which greatly improves the classification efficiency of point cloud data and provides a variety of point cloud data Classification provides new ideas and provides accurate and comprehensive analysis data for power line inspection and other work.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for classifying laser radar point cloud power lines based on normal random sampling distribution. Background technique [0002] As a novel and efficient means of space detection, lidar technology can quickly acquire a large amount of point cloud data with precise three-dimensional spatial coordinates of the target scene in a short period of time. However, compared with the progress made in hardware performance and indicators of lidar systems , the software processing of LiDAR point cloud data is still in its infancy. Faced with the massive point cloud data acquired by the LiDAR hardware system, how to effectively use it is a major problem in the field of LiDAR point cloud data processing. question. At the same time, with the rapid development of the economy in various fields in our country, the demand for electricity in all walks of life is growing very rapidly....

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06T17/00G06F30/27
Inventor 王艳军
Owner HUNAN UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com