Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges

2026-04-27

AI Summary

This study addresses the lack of systematic evaluation of radar odometry in off-road environments, where it faces significant challenges including full SE(3) motion, terrain-induced disturbances, and sparse features. To bridge this gap, the work presents the first comprehensive benchmark of radar odometry on real-world off-road datasets and introduces two simple yet effective baseline methods: Radar-KISSICP, which employs motion compensation to generate dense 3D point clouds, and Radar-IMU, which integrates IMU preintegration to stabilize scan matching. Experimental results on the Great Outdoors dataset demonstrate that both proposed approaches substantially improve trajectory accuracy across complex off-road trajectories, establishing a reliable benchmark and a practical framework for radar-based navigation in off-road robotic applications.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• radar odometry
• off-road
• SE(3) motion
• terrain-induced returns
• sparse features

Innovation

• radar odometry
• off-road robotics
• motion compensation
• IMU preintegration
• SE(3) estimation