Abstract: Due to the advantages of high speed, flexibility and high resolution, helical laser scanners have been widely used in subway tunnel detection systems. However, existing data processing schemes have large synchronization error and low rate of data utilization. We propose a method to improve the accuracy of localization and make full use of the point cloud data. The starting point of the uniform motion of the car is calibrated, and the navigation speed is updated only when exceeding the upper limit, which is determined by the noise of the tunnel road. Meanwhile, the 360° panorama used for disease monitoring is firstly generated from point cloud data to improve the user interaction. Results show that the positioning error is 4.2 mm in 50 m, which is superior to the existing schemes. The generated panorama provides a new developing direction for disease monitoring in subway tunnels.

Key words: subway tunnel, helical scanning, point cloud data, error analysis, disease monitoring