Radar Teach and Repeat：架构与初步实地测试
Radar Teach and Repeat: Architecture and Initial Field Testing
- University of Toronto Robotics Institute（多伦多大学机器人研究所）
- University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对越野恶劣环境下的机器人自主导航需求，提出全栈雷达系统RT&R，无需GPS即可实现路径跟踪，实验验证其性能优异，为长期自主导航提供激光雷达的可行替代方案且已开源。
AI中文摘要：
调频连续波（FMCW）扫描雷达已成为移动机器人状态估计中旋转式激光雷达的替代方案。雷达波长更长，受微小颗粒物影响更小，在沙尘、烟雾、雾气等恶劣环境中具备运行优势。本文提出Radar Teach and Repeat（RT&R，雷达示教与复现）：一套面向长期越野机器人自主导航的全栈雷达系统。RT&R可在无GPS的越野杂乱区域可靠行驶路线。我们对该雷达系统的闭环路径跟踪性能进行基准测试，并将其与对应的3D激光雷达方案对比。仅使用雷达和陀螺仪导航，系统完成了11.8公里的无干预自主驾驶。我们在场景几何结构规整度逐步降低的不同路线上评估RT&R，随着路线难度提升，RT&R的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。在我们用于测试的机器人上，这些RMSE值不到单个轮胎宽度（24厘米）的一半。这些路线的最坏情况误差分别为21.7厘米、24.0厘米和43.8厘米。我们得出结论：在恶劣越野场景下的长期自主导航中，雷达是激光雷达的可行替代方案。RT&R的实现已开源，可访问：https://github.com/utiasASRL/vtr3。
英文摘要：
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.
