  Odometry forms an important component of many manned and autonomous systems.
In the rail industry in particular, having precise and robust odometry is
crucial for the correct operation of the Automatic Train Protection systems
that ensure the safety of high-speed trains in operation around the world. Two
problems commonly encountered in such odometry systems are miscalibration of
the wheel encoders and slippage of the wheels under acceleration and braking,
resulting in incorrect velocity estimates. This paper introduces an odometry
system that addresses these problems. It comprises of an Extended Kalman Filter
that tracks the calibration of the wheel encoders as state variables, and a
measurement pre-processing stage called Sensor Consensus Analysis (SCA) that
scales the uncertainty of a measurement based on how consistent it is with the
measurements from the other sensors. SCA uses the statistical z-test to
determine when an individual measurement is inconsistent with the other
measurements, and scales the uncertainty until the z-test passes. This system
is demonstrated on data from German Intercity-Express high-speed trains and it
is shown to successfully deal with errors due to miscalibration and wheel slip.
