# Radar-only ego-motion estimation in difficult settings via graph matching

## TL;DR

This paper introduces a robust radar-only odometry system that uses graph matching for data association, achieving high accuracy across diverse environments with minimal parameters.

## Contribution

It presents a novel radar odometry pipeline that is resilient to artifacts and adaptable to various settings, utilizing graph matching for data association.

## Key findings

- Achieves approximately 5.20 cm and 0.0929 deg accuracy with GPS ground truth.
- Demonstrates robustness to radar artifacts like speckle noise and false positives.
- Performs well across urban and off-road environments.

## Abstract

Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.