Corridor vanishing point rapid detection algorithm based on K-means method

Abstract

The invention provides a corridor vanishing point rapid detection algorithm based on a K-means method. The method is characterized by carrying out earlier stage processing comprising down-sampling, gray processing, histogram equalization and Canny edge detection after obtaining image data returned by a robot in real time; carrying out line extraction on the image obtained in the previous step by utilizing a probability Hough transformation algorithm; and finally, classifying the detected lines into four kinds by utilizing the K-means algorithm according to the slope, calculating the mean value of the midpoints of each cluster of lines, then, establishing four lines to replace the lines detected in the previous step by utilizing the slope obtained by cluster and the midpoint mean values, dividing the four lines into two groups randomly, calculating the intersection point respectively, and taking the midpoint of the intersection point as a vanishing point. The method can rapidly detect the position of the vanishing point, and then, the heading direction of the robot is corrected by utilizing the vanishing point, so that the navigation of the robot can be realized. Compared with the conventional vanishing point detection method, the vanishing point rapid detection algorithm in the invention has the advantages of being simple and efficient, good in real-time performance and high in stability.

Description

technical field [0001] The invention relates to the field of robot autonomous navigation, specifically a corridor vanishing point detection algorithm based on the K-means method, which can accurately and real-time detect the position coordinates of the vanishing point from the image obtained by the robot, and then provide navigation information for the movement of the robot . The present invention can also be applied to autonomous flight missions of micro-miniature unmanned aerial vehicles along corridors in buildings. Background technique [0002] A group of parallel straight lines in space, the image formed on the image plane has one and only one intersection point, called the vanishing point, which contains the direction information of the parallel straight lines on the image, so it can be used to provide navigation information for robots. This technology has been is widely used. When the robot is moving in the corridor, it uses the real-time image information obtained ...

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Problems solved by technology

[0005] However, the above-mentioned methods have the following disadvantages: (1) In the Gaussian spherical space and Hough space methods, after the Gaussian spherical transformation and Hough transformation, the spatial position information of line segments and vanishing points is reduced, and at the same time, this The accuracy of the vanishing point detected by the two methods is greatly affected by the size of the accumulation unit. When the accumulation unit is large, the error in obtaining the vanishing point is large. When the accumulation unit is too small, the amount of calculation will increase exponentially, which cannot meet the real-time requirements. (2) When detecting the vanishing point on the image plane, all possible line intersections are calculated. This method has high precision, but in practical application, due to the large number of detected lines, the algorithm complexity High, the amount of calculation is large, so the real-time performance is poor, and it cannot meet the actual needs

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