Research on three-dimensional point cloud reconstruction and deformation detection of tunnel contours based on LiDAR
In order to realize comprehensive,efficient and accurate digital detection of subway tunnel contour,a tunnel deformation detection method based on three-dimensional point cloud was proposed in this paper.In this method,the multi-period tunnel contour point cloud data obtained by LiDAR wad fused by positioning algorithm,and a standard tunnel contour model was established by using subway tunnel modeling algorithm to process the fused data.The deformation detection was completed by comparing the measured value with the output value of the model.The positioning algorithm used the mileage mark and track feature data obtained by the speed sensor and the laser displacement sensor to realize coarse and fine calibration positioning of the subway tunnel contour feature data,so as to solve the problem that the laser point cloud data of the same position and different detection periods could not be aligned and fused due to the large positioning error of the speed sensor.The subway tunnel modeling algorithm was based on radial basis neural network(RBFNN),which trained the fused point cloud data multiple times and continuously removed the large error data to establish the common tunnel inner wall model,and combined the clustering algorithm to train the eliminated data to establish the tunnel pipeline region model.The results show that the relative positioning algorithm can realize multi-period data fusion,and the relative positioning error is less than 10 cm.The tunnel modeling algorithm can use the point cloud data to establish the standard tunnel inner wall model to analyze the tunnel deformation and achieve the expected effect.
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