超越路面的雷达里程计：当前能力和挑战
Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges
AI总结：
本文研究雷达在非公路环境中的里程计潜力，提出两个基线方法以应对复杂运动和地面回波问题，实验表明其在挑战性路线中提升轨迹估计效果。
摘要：
雷达在非结构化环境中具有独特优势，包括对天气、光照和空中颗粒的鲁棒性。尽管大多数先前工作研究了城市环境中的雷达里程计，但其在非公路环境中的性能仍不明确。本文探讨了雷达在非公路里程计估计中的潜力，并识别了全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带来的关键挑战。为解决这些问题，我们引入了两个简单基线：Radar-KISSICP通过运动补偿生成3D感知的雷达点云，以及Radar-IMU通过利用IMU预积分来稳定扫描匹配。在Great Outdoors (GO)数据集上的实验表明，这些基线在挑战性路线中提升了轨迹估计效果，并为未来非公路机器人雷达里程计的发展提供了参考点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
