Random sampling consistency-based characteristic line detection method for three-dimensional point cloud

A random sampling and three-dimensional point cloud technology, which is applied to computer parts, instruments, character and pattern recognition, etc., can solve the problems of low time overhead and achieve the effect of eliminating the influence of noise, improving time efficiency and reducing noise

Inactive Publication Date: 2011-04-27
NAT UNIV OF DEFENSE TECH
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0014] The technical problem to be solved by the present invention is to provide a robust method for detecting feature lines in 3D point clouds, which can effectively deal with inevitable noise, outliers and missing data in point cloud data, and has low time overhead , so as to provide a good initial input for various point cloud processing methods based on feature lines

Method used

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  • Random sampling consistency-based characteristic line detection method for three-dimensional point cloud
  • Random sampling consistency-based characteristic line detection method for three-dimensional point cloud
  • Random sampling consistency-based characteristic line detection method for three-dimensional point cloud

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Embodiment Construction

[0068] figure 1 Shown is the general flow chart of the present invention. The main process is:

[0069] Step 1, plane detection based on random sampling consistency.

[0070] Step 2, feature line candidate point extraction, project the points that can be fitted by the detected plane onto the plane, construct a bitmap and select boundary points as candidate points.

[0071] Step 3: Apply random sampling consistency to the candidate points to detect the straight line where the feature line is located.

[0072] Step 4, based on the line segment parameter calculation of Principal Component Analysis, divide the points that can be fitted by each straight line into multiple interconnected areas, and calculate the starting point and end point of the line segment for each area.

[0073] figure 2 It is a flow chart of plane detection based on RANSAC. The main process is:

[0074] Step 1, randomly select in the point cloud P three dots;

[0075] Step 2, according to The posit...

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Abstract

The invention discloses a random sampling consistency-based characteristic line detection method for a three-dimensional point cloud. The method comprises the following steps of: detecting a plurality of planes in the point cloud based on random sampling consistency; and applying the random sampling consistency to boundary points, which are taken as candidate points, of each plane parameterization area so as to obtain a final characteristic line. Therefore, the influences of noise, exterior points and data loss are reduced effectively, the detection robustness of the characteristic line is enhanced greatly and time efficiency is improved.

Description

technical field [0001] The invention relates to a feature extraction method in three-dimensional point cloud data, in particular to a method for effectively extracting feature lines when the point cloud data has relatively large noise and many outliers. Background technique [0002] With the rapid popularization and application of 3D laser scanners in various fields such as reverse engineering, urban modeling, cultural relics protection, geological surveying, and digital entertainment, 3D point cloud data (Point Cloud) has gradually become one of the commonly used representation methods for 3D geometric models. . [0003] Point cloud, as the name implies, is a large number of unorganized and unstructured three-dimensional points (generally coordinate positions, but also can contain other attributes such as normal vectors and colors) to represent the spatial distribution and surface characteristics of three-dimensional objects. There are topological relationships, which are ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46
Inventor 党岗李宝程志全姜巍李宏华陈寅李俊方皓周竞文林帅田艳花金士尧
Owner NAT UNIV OF DEFENSE TECH
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