Study Of Line Structured Light Measurement Technology In The Geometric Parameters Detection Of The Contact Rail

In recent years, major cities are seeking to develop urban rail transit systems, urban rail transit has become most people’s travel choices, urban rail transit system offers great transport capacity for the urban public transport, it has become a important methods to solve to urban congestion. Contact rail has been widely used in the power supply system of urban rail transit, geometry parameters of contact rail have become an important standard to measure whether the power supply system of subway is normal or not. When the geometry parameters is not normal, it will have an impact on the power supply of subway locomotive, a serious accident could occur. Therefore, the detection of geometric parameters on urban transport rail system has a important significance. Relative to manual detection mode, the automatic detection system has the advantages of high effective rate and high accuracy, the automatic detection system plays an important role in the metro power supply system. This paper was wrote based on the two indicators for detection of contact rail geometric parameters of high precision and high efficiency, and this paper carried out deep research on the detection system of contact rail geometric parameters. This paper combined with laser triangulation principle and the principle of contract rail geometry detection, and analyzed the key parameters in solving contract rail geometry parameters.This paper contact rail detection system uses four laser imaging sensor to collect the images of contact rail and running rail, then analyze the feature points of the collected images, solve the true distance of the camera center between the feature points. This paper used the laser triangulation principle twice to turn the values we measured to the parameters that we have to solve, then we get the contact rail geometric parameter that we desired.This paper established two models between world coordinate system and the image coordinate system. One is the linear model, it didn’t consider the distortion factor of the camera lens. The other one is the non-linear model, it took the distortion factor of the camera lens into consideration.To solve the distance between the feature points of the collected images by the contact rail system and the center of camera, we must standardize the camera firstly. Camera standardization requires that we should detect the feature points of the standardization images accurately. This paper describes the Harris corner detection algorithm, and analysis the merits of the algorithm. This paper improved the Harris corner detection algorithm to achieve the corner detection on pixel level. Aiming at the black-white checkerboard picture used by standardization, the improved algorithm by this paper can detect the position of all the corners. This paper studies the sub-pixel corner extraction based on interpolation method, gray gradient method, gray square gravity method, and combine the three method with the improved corner detection method. This paper used three method to detect the sub-pixel corner, the test results showed that the error data of interpolation method is minimum, the error can be controlled within 0.5mm. Standardizing the camera with the coordinate data obtained by the sub-pixel corner detection test. The error data of the linear model was less than 2mm, the error data of the non-linear model was less than 0.5mm.Because of the collected image by the contact rail geometry parameters detection system is line structured light image, combining with features of line structured light and digital image processing method, this paper did image preprocessing to the line structured light image to reduce the interference of the noise. Then this paper do edge detection to the line structured light image. This paper conducted a test to extract the center of line structured light stripe on the basis of gray-gravity method and the Gaussian curve-fitting method. The tests show that the result of the Gaussian curve-fitting method is better than the gray-gravity. This paper improved the gray-gravity method, and used this method to extract the center of line structured light stripe. The result of the improved method is better than the Gaussian curve-fitting.Finally, this paper do repeatability test to detect the reliability of the improved method based on the site measured experimental data of the A city subway line 4.