探索雷达在非铺装路面的定位能力，发现三大挑战并提出有效解决方案。
Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges
- 通过运动补偿生成3D雷达点云，提升环境感知精度。
- 利用IMU预积分稳定扫描匹配，轨迹误差降低30%以上。
- 为无人越野机器人提供可靠雷达里程计基准方法。
雷达在非结构化环境中具有抗天气、光照和空气颗粒物干扰的天然优势。尽管以往研究多聚焦于城市等平坦场景，但其在非铺装路面的表现仍不清晰。本文研究雷达在非铺装环境下的里程计估计潜力，识别出三个核心挑战：全$SE(3)$车辆运动、地形引起的地面回波以及稀疏或不稳定特征。为此，提出两个简单基线：Radar-KISSICP通过运动补偿生成3D感知雷达点云；Radar-IMU则利用IMU预积分稳定扫描匹配。在Great Outdoors（GO）数据集上的实验表明，这些方法在复杂路径下显著改善了轨迹估计性能，为未来非铺装环境下雷达里程计的发展提供了参考基准。
原文摘要 · Abstract (English)
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.
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