# Robust Odometry using Sensor Consensus Analysis

arXiv: https://arxiv.org/abs/1803.02237 · HTML: https://mlsanity.com/paper/1803.02237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping](https://mlsanity.com/paper/2102.08145/) (2021)
- [The Autonomous Siemens Tram](https://mlsanity.com/paper/2102.04034/) (2021)

## Abstract (arXiv)

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.