Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges

TL;DR

This paper explores the use of radar for off-road vehicle localization, addressing unique challenges and proposing baseline methods that improve trajectory estimation in unstructured environments.

Contribution

It introduces two simple baseline approaches, Radar-KISSICP and Radar-IMU, for enhancing radar odometry in off-road scenarios, a less-studied environment.

Findings

Baseline methods improve trajectory accuracy in challenging off-road routes.

Radar-based odometry can be stabilized using motion compensation and IMU data.

The study provides a reference for future off-road radar odometry research.

Abstract

Radar offers unique advantages for localization in unstructured environments, including robustness to weather, lighting, and airborne particulates. While most prior work has studied radar odometry in urban, largely planar settings, its performance in off-road environments remains less understood. In this paper, we investigate the potential of radar for off-road odometry estimation and identify key challenges that arise from full $SE(3)$ vehicle motion, terrain-induced ground returns, and sparse or unstable features. To address these issues, we introduce two simple baselines: Radar-KISSICP, which applies motion compensation to generate 3D-aware radar pointclouds, and Radar-IMU, which leverages IMU preintegration to stabilize scan matching. Experiments on the Great Outdoors (GO) dataset demonstrate that these baselines improve trajectory estimation in challenging routes and provide a reference point for future development of radar odometry in off-road robotics.