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.

翻译：雷达在非结构化环境中的定位具有独特优势，包括对天气、光照和空气中颗粒物的鲁棒性。尽管以往研究多聚焦于城市平坦场景下的雷达里程计，但其在越野环境中的性能仍待深入探究。本文旨在探索雷达用于越野里程估计的潜力，并识别由完整 $SE(3)$ 车辆运动、地形诱发的地面回波以及稀疏或不稳定特征带来的关键挑战。针对这些问题，我们提出两种简单基线方法：Radar-KISSICP（通过运动补偿生成三维感知雷达点云）与Radar-IMU（利用IMU预积分稳定扫描匹配）。在Great Outdoors (GO)数据集上的实验表明，这些基线方法可改善复杂路径的轨迹估计，并为未来越野机器人雷达里程计的发展提供参考基准。