A Seamless Train Positioning System Using a Lidar-Aided Hybrid Integration Methodology

Abstract

GNSS/INS integrated navigation and positioning technology is widely used for train operations applications seeking a decrease in dependence on trackside equipment. However, during train operations, environmental factors such as bridges, tunnels, hillsides, and buildings will block the satellite signals. When INS works in a stand-alone mode, the system errors accumulate with time due to the inertial sensor characteristics. To overcome this problem, light detection and ranging (lidar) equipment can be introduced to help control INS divergence. The lidar point cloud data is first processed using the random sample consensus (RANSAC) algorithm to determine the track plane, then the moving average filtering (MAF) algorithm is used to detect and classify the rails. The frogs can be detected based on the changing rail number and the constraint condition for turnout parameters. With the assistance of a previously surveyed turnout database, the geographical positions of the detected frogs can be obtained and integrated with INS data to converge INS sensor errors when GNSS is not available. In this paper a lidar-aided GNSS/INS integrated navigation system is proposed that can ensure continuous positioning in both GNSS-available and GNSS-blocked scenarios. GNSS/INS is applied when the system operates in the GNSS-available scenario, and switches to the INS/lidar mode when the GNSS signals are blocked. To evaluate the performance of the proposed system, an actual train experiment was conducted along the Qinghai-Tibet railway. The results confirm that the proposed lidar-aided navigation system can achieve accurate and continuous positioning. When GNSS is not available, the proposed method can effectively suppress the error of INS with a DRMS of 1.66 m, which is a 70.4% improvement compared with a conventional GNSS/INS integrated system.