A New Filtering and Smoothing Algorithm for Railway Track Surveying Based on Landmark and IMU/Odometer
AbstractHigh-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the IMU/odometer and landmarks integration for the railway track surveying. In order to overcome the difficulty of estimating too many error parameters with too few landmark observations, a new model with completely observable error states is established by combining error terms of the system. Based on covariance analysis, the analytical relationship between the railway track surveying accuracy requirements and equivalent gyro drifts including bias instability and random walk noise are established. Experiment results show that the accuracy of the new filtering and smoothing algorithm for railway track surveying can reach 1 mm (1σ) when using a Ring Laser Gyroscope (RLG)-based Inertial Measurement Unit (IMU) with gyro bias instability of 0.03°/h and random walk noise of 0.005 °h while control points of the track control network (CPIII) position observations are provided by the optical total station in about every 60 m interval. The proposed approach can satisfy at the same time the demands of high accuracy and work efficiency for railway track surveying. View Full-Text
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Jiang, Q.; Wu, W.; Jiang, M.; Li, Y. A New Filtering and Smoothing Algorithm for Railway Track Surveying Based on Landmark and IMU/Odometer. Sensors 2017, 17, 1438.
Jiang Q, Wu W, Jiang M, Li Y. A New Filtering and Smoothing Algorithm for Railway Track Surveying Based on Landmark and IMU/Odometer. Sensors. 2017; 17(6):1438.Chicago/Turabian Style
Jiang, Qingan; Wu, Wenqi; Jiang, Mingming; Li, Yun. 2017. "A New Filtering and Smoothing Algorithm for Railway Track Surveying Based on Landmark and IMU/Odometer." Sensors 17, no. 6: 1438.
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