For autonomous ground vehicles (AGVs) deployed in suburban neighborhoods and other human-centric environments the problem of localization remains a fundamental challenge. There are well established methods for localization with GPS, lidar, and cameras. But even in ideal conditions these have limitations. GPS is not always available and is often not accurate enough on its own, visual methods have difficulty coping with appearance changes due to weather and other factors, and lidar methods are prone to defective solutions due to ambiguous scene geometry. Radar on the other hand is not highly susceptible to these problems, owing in part to its longer range. Further, radar is also robust to challenging conditions that interfere with vision and lidar including fog, smoke, rain, and darkness. We present a radar-based localization system that includes a novel method for highly-accurate radar odometry for smooth, high-frequency relative pose estimation and a novel method for radar-based place recognition and relocalization. We present experiments demonstrating our methods' accuracy and reliability, which are comparable with \new{other methods' published results for radar localization and we find outperform a similar method as ours applied to lidar measurements}. Further, we show our methods are lightweight enough to run on common low-power embedded hardware with ample headroom for other autonomy functions.

翻译：对于部署在城郊社区及其他以人为中心环境中的自主地面车辆（AGVs）而言，定位问题仍是一项基础性挑战。目前已有成熟的定位方法，包括基于GPS、激光雷达和相机的方案。但即便在理想条件下，这些方法也存在局限性。GPS并非始终可用且单独使用精度不足，视觉方法难以应对天气等因素造成的外观变化，而激光雷达方法因场景几何结构模糊易导致解算失效。相比之下，雷达对这些问题的敏感性较低，部分得益于其较长的探测距离。此外，雷达对干扰视觉与激光雷达的恶劣条件（包括雾、烟、雨和黑暗）具有鲁棒性。我们提出了一套基于雷达的定位系统，包含用于平滑高频相对位姿估计的高精度雷达里程计新方法，以及基于雷达的地点识别与重定位新方法。实验表明，所提方法的精度与可靠性可与其他雷达定位方法的已发表结果媲美，且我们发现其性能优于将类似方法应用于激光雷达测量的方案。进一步地，我们证明该方法足够轻量化，可在常见低功耗嵌入式硬件上运行，并留有充足余量支持其他自主功能。