Robust Odometry using Sensor Consensus Analysis

TL;DR

This paper presents a robust odometry system for high-speed trains that combines an Extended Kalman Filter with Sensor Consensus Analysis to handle miscalibration and wheel slip, improving accuracy and safety.

Contribution

It introduces Sensor Consensus Analysis (SCA) for measurement uncertainty scaling and integrates it with an EKF for improved odometry robustness in rail systems.

Findings

Successfully handles wheel encoder miscalibration.

Effectively manages wheel slip during acceleration and braking.

Demonstrated on high-speed train data with improved accuracy.

Abstract

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.