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Robot Obstacle Recognition Method Based on Gradient Histogram and Support Vector Machine

A technology of support vector machine and gradient histogram, which is applied in the field of robot obstacle recognition, can solve the problems that the reliability and effectiveness cannot be guaranteed, and block the progress of the line patrol robot, so as to reduce the amount of calculation, reduce the amount of irrelevant features, and the amount of calculation low effect

Active Publication Date: 2019-08-06
STATE GRID INTELLIGENCE TECH CO LTD
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Problems solved by technology

However, when the line inspection robot uses a wheeled walking mechanism to "crawl" along the overhead transmission line, the tower support accessories installed on the wires such as shockproof hammers, insulators, suspension line clamps, and tension line clamps block the progress of the line inspection robot.
At the same time, different obstacles have relatively obvious changes in posture and angle of view, coupled with complex field inspection scene backgrounds, large-scale changes in lighting angles, and shaking of the visual system itself, the problem of robust online object recognition has always been is a challenging research topic
However, the defect of existing statistical techniques is that under the influence of large-scale complex background and illumination, there will inevitably be a large number of false detection results, so the reliability and validity cannot be guaranteed.

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  • Robot Obstacle Recognition Method Based on Gradient Histogram and Support Vector Machine
  • Robot Obstacle Recognition Method Based on Gradient Histogram and Support Vector Machine
  • Robot Obstacle Recognition Method Based on Gradient Histogram and Support Vector Machine

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[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] Such as figure 1 As shown, an obstacle recognition method for line inspection robot based on gradient histogram and support vector machine, the following steps are carried out:

[0045] Step 1: Extract the gradient histogram features of the original image, and determine the feature vector sets representing obstacles on different lines, which specifically includes the following three steps:

[0046] Step 1: Divide a 64×128 size picture, and designate a 4×4 size pixel area as a unit. we use G x (x,y), G y (x, y) represent the gradient magnitudes in the horizontal direction and vertical direction at the pixel point (x, y) respectively, G(x, y) represents the gradient magnitude at the pixel point (x, y), α(x, y) Indicates the gradient direction at the pixel point (x, y).

[0047] G x (x,y)=I(x+1,y)-I(x-1,y)

[0048] G y (x,y)=I(x,y+1)-I(x,y-1...

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Abstract

The invention discloses a robot obstacle recognition method based on gradient histogram and support vector machine. The method mainly includes two parts: first, in the feature extraction stage, the feature of the transmission line obstacle of the principal component gradient histogram is proposed extraction algorithm. Utilizing the characteristics of typical obstacles with different structural compositions and spatial layouts, by calculating the statistical characteristics of common online obstacles and using the feature extraction of the HOG algorithm, feature points that are independent of illumination and scale changes can be obtained, which can effectively remove interference. At the same time, the principal component analysis is used to further reduce the dimensionality of the obtained eigenvectors to obtain the principal component gradient histogram, which can effectively reduce irrelevant features, reduce the amount of calculation, and use the least amount of features to establish a feature set corresponding to obstacles. One-step target identification provides good support. Second, in the stage of target recognition, a linear support vector machine (SVM) is used for recognition, and a very good recognition effect is obtained.

Description

technical field [0001] The invention relates to a robot obstacle recognition method based on a gradient histogram and a support vector machine. Background technique [0002] The autonomous navigation system of transmission line inspection robots has always been one of the research hotspots in smart grid maintenance and safety monitoring. It has broad application prospects in the fields of transmission line inspection, maintenance, rapid fault location, and online monitoring. However, when the line inspection robot uses a wheeled walking mechanism to "crawl" along the overhead transmission line, the anti-vibration hammers, insulators, suspension line clamps, tension line clamps and other pole support accessories installed on the wires block the progress of the line inspection robot. At the same time, different obstacles have relatively obvious changes in posture and angle of view, coupled with complex field inspection scene backgrounds, large-scale changes in lighting angles,...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/00
CPCG06V20/40G06V2201/07G06F18/2411
Inventor 张峰郭锐慕世友任杰傅孟潮雍军韩正新程志勇贾永刚曹雷贾娟李建祥赵金龙
Owner STATE GRID INTELLIGENCE TECH CO LTD
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