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Optimized ORB algorithm based on combination of RGB-D camera and plane detection and random sampling consistency algorithm

A technology with consistent sampling and algorithm, applied in the field of optimizing ORB algorithm, can solve the problems of poor practicability, large amount of calculation, and increased cost of mobile robots, and achieve the effect of improving positioning accuracy and real-time performance, improving accuracy and improving accuracy.

Pending Publication Date: 2020-12-22
NANJING UNIV OF TECH
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  • Summary
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AI Technical Summary

Problems solved by technology

This method can improve the efficiency of generating the optimal solution, but due to the large amount of calculation, it is less practical for mobile robots, which increases the cost

Method used

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  • Optimized ORB algorithm based on combination of RGB-D camera and plane detection and random sampling consistency algorithm
  • Optimized ORB algorithm based on combination of RGB-D camera and plane detection and random sampling consistency algorithm
  • Optimized ORB algorithm based on combination of RGB-D camera and plane detection and random sampling consistency algorithm

Examples

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Effect test

Embodiment 1

[0054] like figure 1 As shown, an optimized ORB algorithm based on RGB-D camera combined with plane detection and random sampling consensus algorithm, the method includes the following steps:

[0055] S1: Use an RGB-D camera to acquire image data, including color images and depth images;

[0056] S2: Use the ORB algorithm to extract feature points from the image data, and use the feature point uniformity evaluation method to judge the feature point distribution uniformity;

[0057] S3: For the part of the image data where the feature points are evenly distributed, generate a point cloud and downsample it;

[0058] S4: Perform plane detection and extraction on the down-sampled point cloud, and use the random sampling consensus algorithm to eliminate false matches;

[0059] S5: For the part of the image data with uneven distribution of feature points, use the set threshold for feature point extraction and the non-maximum value suppression method to eliminate overlapping featur...

Embodiment 2

[0104] The difference between this embodiment and Embodiment 1 is: in S21, m=9; in S42, if the remaining points of the point cloud are greater than 40% of the total or the extraction planes are less than 5, feature points are extracted.

Embodiment 3

[0106] The difference between this embodiment and Embodiment 1 is: in S21, m=10; in S42, feature points are extracted if the number of remaining points in the point cloud is greater than 20% of the total or the extraction plane is less than 4.

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Abstract

The invention provides an optimized ORB algorithm based on combination of an RGB-D camera and a plane detection and random sampling consistency algorithm. The method comprises the following steps: S1,obtaining image data through the RGB-D camera, wherein the image data comprises a color image and a depth image; S2, performing feature point extraction on the image data by using an ORB algorithm, and judging the distribution uniformity of feature points by using a feature point uniformity evaluation method; S3, for the image data part with uniformly distributed feature points, generating a point cloud, and downsampling on the point cloud; S4, carrying out plane detection extraction on the point cloud after downsampling, and eliminating mismatching by using a random sampling consistency algorithm; and S5, for an image data part with non-uniform feature point distribution, performing feature point extraction by using a set threshold, and eliminating overlapped feature points by using a non-maximum suppression method. The calculated amount can be reduced, the accuracy of feature point extraction is improved, and mismatching is reduced, so that the requirements of a mobile robot on accuracy and real-time performance are met.

Description

technical field [0001] The invention is an optimized ORB algorithm based on RGB-D camera combined with plane detection and random sampling consistent algorithm, and belongs to the technical field of indoor mobile robot path planning and navigation. Background technique [0002] In recent years, intelligent mobile robot technology has developed rapidly and has been widely used in industries such as industry, military, logistics, office and home services. With the advent of RGB-D sensors, the research on mobile robot localization or SLAM using RGB-D sensors has developed rapidly. The advancement of related technologies such as image processing and point cloud processing, as well as the advantages of RGB-D sensors with rich information acquisition, non-contact measurement, easy installation and use, and low cost, make RGB-D sensors widely used in target recognition, tracking, etc. field. The first problem in the robot navigation problem is how to determine the scene model. In...

Claims

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Application Information

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IPC IPC(8): G06K9/46
CPCG06V10/40G06V10/44Y02T10/40
Inventor 程明司雨晨
Owner NANJING UNIV OF TECH
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