Estimation of the trajectory and attitude of railway vehicles using inertial sensors with application to track geometry measurement

This paper describes a novel method for the estimation of the trajectory and orientation of a rigid body moving along a railway track. Compared to other recent developments in the literature, the presented approach has the significant advantage of using inertial sensors only, excluding global position and orientation sensors. The excluded sensors are compensated with an odometry system and previous knowledge of the design track geometry. The procedure is based on a kinematic model of the relative motion of the body with respect to the track, together with a Kalman filter algorithm. Two different approaches are used and compared for the estimation of the noise covariance matrices in the Kalman filter. One is based on the use of experimental results with a known output. The other one relies upon constrained maximum likelihood estimation. The calculated trajectory and orientation are applied in this research to the problem of track geometry measurement. A scale track is used for experimental validation, showing that results are sufficiently accurate for this application. The obtained results also reveal that the constrained maximum likelihood estimation performs similarly to the known-output method. This is very convenient because it allows a straightforward application of the algorithm in different scenarios.