Radar Teach and Repeat: Architecture and Initial Field Testing

TL;DR

This paper introduces Radar Teach and Repeat (RT&R), a radar-based system enabling long-term off-road robot navigation without GPS, demonstrating competitive path-tracking accuracy and robustness in challenging environments compared to LiDAR.

Contribution

The paper presents a full-stack radar system for autonomous off-road navigation, including architecture, implementation, and initial field testing results, highlighting radar's viability as an alternative to LiDAR.

Findings

11.8 km autonomous driving without intervention

Path-tracking RMSE less than 8 cm in structured routes

Radar achieves comparable accuracy to LiDAR in challenging environments

Abstract

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.