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This paper investigates the performance of radar odometry in unstructured, off-road environments, highlighting challenges such as full SE(3) motion and terrain-induced ground returns. They introduce two baseline methods: Radar-KISSICP, which uses motion compensation for 3D pointclouds, and Radar-IMU, which uses IMU preintegration to stabilize scan matching. Experiments on the GO dataset show improved trajectory estimation with these baselines, establishing a benchmark for future off-road radar odometry research.
Radar odometry, typically confined to urban settings, can be pushed off-road with simple adaptations like IMU preintegration, but still faces significant challenges in unstructured environments.
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.
