A Digital Track Map-Assisted SINS/OD Fusion Algorithm for Onboard Train Localization

Abstract

Accurate and reliable speed and position estimation plays an important role in the safety and efficiency of intelligent railway vehicles. Due to the level required of safety, reliability, and strong norms in the current practical application, intelligent railway vehicle positioning heavily relies on a large number of balises laid on the track and the onboard odometer (OD), while the other position method, GNSS introduction, is relatively slow. This article proposed a digital track map-assisted onboard railway location system using strapdown inertial navigation system (SINS) and OD. The proposed method consists of two steps. First, an SINS- and OD-integrated navigation method based on OD velocity integration is in the inner circle. Then, a map-matching algorithm based on vertical projection and heading weighting was employed, and when the matching outer circle results were obtained, the positions obtained from the matching outer circles were used to replace the positions obtained from SINS/OD for the Kalman filter combination. The performance of our algorithm was verified using field tests, and SINS/OD and SINS/OD/MM comparison data processing results prove that our proposed digital track map-assisted SINS/OD algorithm can effectively suppress the accumulation of train position errors. After nearly 80 km of navigation, the position error is 24 m, and the relative mileage accuracy is less than or equal to 0.03% distance.

Study of Tunnel Vehicle GNSS/INS/OD Combination Position Based on Lateral Distance Measurement and Lane Line Constraint

“…How to distinguish between valid data and noise reduce the impact of vibration noise on system stability and positioning accuracy rem to be studied [18][19][20]. In different scenarios, when the effectiveness of sensors in the grated navigation system changes, corresponding adjustments need to be made to state space model [21,22]. Multi-sensor systems are often heterogeneous due to thei ferent principles, making it a challenge to construct reliable multi-sensor fusion fun models that can accommodate the real-time addition and removal of sensor types [23 Based on this, this paper uses high-precision digital lane maps, as well as the vantages of LiDAR in distance measurement and odometry in mileage counting, to pose a combined GNSS/INS/OD positioning method for tunnel vehicles based on la distance measurement and lane line constraints.…”

“…How to distinguish between valid data and noise and reduce the impact of vibration noise on system stability and positioning accuracy remains to be studied [18][19][20]. In different scenarios, when the effectiveness of sensors in the integrated navigation system changes, corresponding adjustments need to be made to the state space model [21,22]. Multi-sensor systems are often heterogeneous due to their different principles, making it a challenge to construct reliable multi-sensor fusion function models that can accommodate the real-time addition and removal of sensor types [23,24].…”