| CPC G06T 7/74 (2017.01) [B61L 27/57 (2022.01); G06T 7/0002 (2013.01); G06T 7/80 (2017.01); G06T 2207/10028 (2013.01)] | 4 Claims |
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1. A method for gauge detection of a rail vehicle based on three-dimensional point cloud data, comprising following steps:
S1, building: building a gauge detection gate at a predetermined position wherein the rail vehicle leaves a garage, and installing a profilometer on the gauge detection gate, wherein the profilometer comprises a plurality of laser camera modules, a proximity switch and a speed measuring unit are further installed on the gauge detection gate or at the predetermined position wherein the rail vehicle leaves the garage;
verifying whether a factory calibration result of each of the laser camera modules is within a calibration expected range, and if the factory calibration result conforms to the calibration expected range, directly executing a process of multi-module calibration; and
if the factory calibration result does not conform to the calibration expected range, firstly, using calibration reference to perform single-module calibration on each of the laser camera modules in turn, and then performing the process of the multi-module calibration after all the laser camera modules complete the single-module calibration, wherein the calibration reference comprises a sawtooth calibration block vertically set on a horizontal plane;
a process of the single-module calibration comprises:
correcting calibration: adjusting a position of the sawtooth calibration block, so the sawtooth calibration block and a line laser emitted by each of the laser camera modules are located on a same vertical axis;
collecting images: adjusting a distance between each of the laser camera modules and the sawtooth calibration block, and collecting N images at positions with N different distances, wherein N≥1;
starting calibration: respectively calculating positions of feature points of peaks and valleys of the N images in an image coordinate system according to straight line fitting and a formula of straight line intersection calculation, then calculating a transformation relationship from a pixel coordinate system of each of the laser camera modules to a sawtooth calibration block coordinate system according to known actual sizes between the peaks and the valleys, and saving data of the transformation relationship as a single-module calibration file;
S2, calibrating: performing the multi-module calibration on all the laser camera modules built by using a calibration structure, wherein the calibration structure is a frame formed by a plurality of sawtooth calibration blocks, and an upper part and both sides of the calibration structure are the sawtooth calibration blocks connected end to end;
recording current calibration parameters of all the laser camera modules, and using the current calibration parameters as a point cloud stitching basis for subsequent real rail vehicle images;
by performing the multi-module calibration on all the built laser camera modules, transforming a laser camera module coordinate system into a calibration structure coordinate system;
S2-1, loading 3D profiles collected by all the laser camera modules;
S2-2, extracting bevel data of a sawtooth, fitting extracted bevel data into a straight line, and then calculating to obtain an intersection of two oblique lines, thus obtaining 3D coordinates of a sawtooth vertex in the laser camera module coordinate system;
S2-3, obtaining 3D coordinates of a sawtooth point in the calibration structure coordinate system according to a physical size of the calibration structure;
S2-4, calculating a rotation and translation transformation matrix, calculating to obtain a transformation relationship from the laser camera module coordinate system to the calibration structure coordinate system, and transforming a sawtooth line from the laser camera module coordinate system to the calibration structure coordinate system, so the sawtooth line and the sawtooth point coincide in a predetermined range; and
S2-5, saving the current calibration parameters as a multi-module calibration file for subsequent multi-camera point cloud stitching;
S3, detecting: when the rail vehicle undergoing gauge detection passes through the gauge detection gate, sensing a vehicle speed of a current rail vehicle by the speed measuring unit in real time and reporting to a processing unit; and
performing a full section scan of an outer contour of the rail vehicle by the profilometer to generate a three-dimensional point cloud map of the current rail vehicle;
S4, comparing results: comparing the three-dimensional point cloud map of the current rail vehicle generated in the S3 with built-in standard gauge contour data to judge whether the current rail vehicle is out-of-gauge; and
S5, outputting results: according to comparison results of the S4, if the current rail vehicle is not out-of-gauge, sending a notice of not out-of-gauge; and
if the current rail vehicle is out-of-gauge, sending a notice of out-of-gauge, and providing out-of-gauge parameters of the current rail vehicle.
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