纯雷达实现无GPS越野机器人长期自主导航，精度超轮胎宽度一半。
Radar Teach and Repeat: Architecture and Initial Field Testing
- 用调频连续波雷达构建全栈系统，无需激光雷达与GPS
- 11.8公里无干预行驶，路径误差均小于5.6厘米（最差43.8厘米）
- 适合复杂地形、粉尘烟雾等恶劣环境下的机器人研发者使用
调频连续波（FMCW）扫描雷达正成为移动机器人状态估计的新型替代方案，其较长波长对微粒干扰不敏感，在尘土、烟雾和雾天等恶劣环境中更具优势。本文提出雷达教学与重播（RT&R）系统：一套面向长期非结构化越野场景的全栈雷达自主系统。该系统可在无任何GPS条件下可靠行驶于复杂非结构化区域。我们评估了其闭环路径跟踪性能，并与3D激光雷达方案对比。测试中仅依赖雷达与陀螺仪完成了11.8公里自主驾驶，全程无干预。随着路线几何结构逐渐退化，横向路径跟踪均方根误差（RMSE）分别为5.6厘米、7.5厘米和12.1厘米，最差情况分别达21.7厘米、24.0厘米和43.8厘米。在实验机器人上，这些误差均低于单个轮胎宽度（24厘米）的一半。结果表明，雷达是复杂非结构化越野场景下实现长期自主的可行替代方案。RT&R系统已开源，项目地址：https://github.com/utiasASRL/vtr3。
原文摘要 · Abstract (English)
Frequency-modulated continuous-wave (FMCW) scanning radar has emerged as an alternative to spinning LiDAR for state estimation on mobile robots. Radar's longer wavelength is less affected by small particulates, providing operational advantages in challenging environments such as dust, smoke, and fog. This paper presents Radar Teach and Repeat (RT&R): a full-stack radar system for long-term off-road robot autonomy. RT&R can drive routes reliably in off-road cluttered areas without any GPS. We benchmark the radar system's closed-loop path-tracking performance and compare it to its 3D LiDAR counterpart. 11.8 km of autonomous driving was completed without interventions using only radar and gyro for navigation. RT&R was evaluated on different routes with progressively less structured scene geometry. RT&R achieved lateral path-tracking root mean squared errors (RMSE) of 5.6 cm, 7.5 cm, and 12.1 cm as the routes became more challenging. On the robot we used for testing, these RMSE values are less than half of the width of one tire (24 cm). These same routes have worst-case errors of 21.7 cm, 24.0 cm, and 43.8 cm. We conclude that radar is a viable alternative to LiDAR for long-term autonomy in challenging off-road scenarios. The implementation of RT&R is open-source and available at: https://github.com/utiasASRL/vtr3.
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