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

LiDar point cloud data morphological filtering method based on area prediction

A technology of morphological filtering and point cloud data, which is applied in image data processing, instruments, calculations, etc., can solve the problems of weak self-adaptation and achieve good filtering results, high efficiency, and fast speed

Inactive Publication Date: 2014-04-23
XIDIAN UNIV
View PDF2 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to address the shortcomings of weak self-adaptation due to manual setting of the filtering threshold in the point cloud data filtering method based on morphology in the prior art, and propose a morphological LiDAR point cloud data filtering algorithm based on region prediction

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 data morphological filtering method based on area prediction
  • LiDar point cloud data morphological filtering method based on area prediction
  • LiDar point cloud data morphological filtering method based on area prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] In this embodiment, the method of the present invention is used to perform filtering simulation on the test sample sample 12 provided by ISPRS (International Society for Photogrammetry and Remote Sensing).

[0058] The simulation conditions are carried out under MATLAB2010a software.

[0059] Reference figure 1 , Perform a simulation experiment on the test sample sample12, which contains 52119 points of point cloud data. figure 1 (a) is the initial surface model (DSM), which reflects the original topography of the experimental area; figure 1 (b) Display the distribution of the ground point cloud in the sample data, all blanks are the ground feature points that should be filtered out; figure 1 (c) is the distribution result of the ground point cloud after filtering by this algorithm; figure 1 (d) is the final digital elevation model (DEM).

[0060] From figure 1 (a) It can be seen that the data feature of samp12 is a mixture of buildings and vegetation on the hillside, and...

Embodiment 2

[0062] In this embodiment, the method of the present invention is used to simulate the filtering of the test sample sample31 provided by ISPRS.

[0063] Simulation 2 simulation conditions are carried out under MATLAB2010a software.

[0064] Reference figure 2 , Perform simulation experiments on the test sample sample31, which contains 28862 points of point cloud data.

[0065] figure 2 (a) is DSM, reflecting the original topography of the experimental area; figure 2 (b) Display the distribution of the ground point cloud in the sample data, all blanks are the ground feature points that should be filtered out; figure 2 (c) is the distribution result of the ground point cloud after filtering in the present invention; figure 2 (d) is the final DEM generated.

[0066] From figure 2 (a) It can be seen that sample31 is mainly discontinuous terrain and low points, including complex buildings, vegetation and other targets. From figure 2 (c) and figure 2 (b) It can be seen that the effe...

Embodiment 3

[0068] In this embodiment, the method of the present invention is used to simulate the filtering of the test sample sample53 provided by ISPRS.

[0069] Simulation 3 simulation conditions are carried out under MATLAB2010a software.

[0070] Reference image 3 , Perform simulation experiments on the test sample sample53, which contains 34378 points of point cloud data. image 3 (a) is DSM, reflecting the original topography of the experimental area; image 3 (b) Display the distribution of the ground point cloud in the sample data, all blanks are the ground feature points that should be filtered out; image 3 (c) is the distribution result of the ground point cloud after filtering by this algorithm; image 3 (d) is the final DEM generated.

[0071] From image 3 (a) It can be seen that the data of samp53 is mainly discontinuous terrain with few buildings, but its data structure is more complicated, the discontinuous steep slopes are separated by layers, and buildings are distributed on...

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 relates to a LiDAR point cloud data morphological filtering method based on area prediction. The method includes the following steps: step one, building a filtering initial surface model; step two, removing gross error points of the initial surface model, and obtaining a filtering surface model; step three, performing partitioning on the filtering surface model; and step four, performing filtering processing. The method in the invention overcomes the defect in the prior art that morphological filtering cannot well realize filtering of complex scenes, and the problem that self-adaption is not strong which is caused by filtering processing using constant terrain parameters set manually, the partitioning principle and the advantage that using predicted terrain parameters of partitioned areas can adjust an altitude difference threshold value according to the up-and-down conditions of terrains are utilized, so that filtering on point cloud data can be performed in a self-adaption manner, and a relatively good filtering result can be obtained at last.

Description

Technical field [0001] The invention belongs to the technical field of airborne LiDAR data processing, and further relates to a morphological point cloud data filtering algorithm based on region prediction in the technical field of airborne LiDAR data filtering. It can be applied to the point cloud data obtained by airborne LiDAR scanning the ground, which can effectively filter the point cloud data and extract the DEM. Background technique [0002] Airborne LiDAR is an active remote sensing technology that can scan a large area to directly obtain three-dimensional information on the ground. Its data has the characteristics of high precision, high density, and irregular discreteness. [0003] LiDAR point cloud data contains a variety of information such as real ground, buildings, and vegetation. Filtering the point cloud data to obtain real ground points and forming a digital elevation model (DEM) is of great significance. At present, many point cloud filtering algorithms adopt fi...

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
IPC IPC(8): G06T5/00G06T5/30
Inventor 苗启广宋建锋郭雪许鹏飞陈为胜宣贺君刘如意张萌
Owner XIDIAN UNIV
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