Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges

arXiv cs.RO / 4/28/2026

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Key Points

The paper explores radar odometry for off-road localization, highlighting that radar’s robustness can help in unstructured environments but that off-pavement performance is still poorly understood.
It identifies key technical challenges specific to full 3D vehicle motion in SE(3), including terrain-induced ground returns and the presence of sparse or unstable radar features.
To tackle these issues, the authors propose two baseline approaches: Radar-KISSICP, which performs motion compensation to create 3D-aware radar point clouds, and Radar-IMU, which uses IMU preintegration to stabilize scan matching.
Experiments on the Great Outdoors (GO) dataset show that both baselines improve trajectory estimation on difficult routes and establish reference results for future radar odometry work in off-road robotics.

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.