Toward Teach and Repeat Across Seasonal Deep Snow Accumulation

TL;DR

This paper explores the feasibility of teach and repeat autonomous navigation in seasonal deep snow environments using lidar and radar, highlighting challenges and potential solutions for reliable off-road operation across seasons.

Contribution

It presents preliminary field trials with lidar and radar for teach and repeat in seasonal snow, analyzing localization performance over time and identifying key challenges.

Findings

Lidar-based teach and repeat performs well with ground point removal.

Radar can localize on older maps but struggles with recent maps under certain conditions.

High vehicle pitch or roll can cause radar localization failures.

Abstract

Teach and repeat is a rapid way to achieve autonomy in challenging terrain and off-road environments. A human operator pilots the vehicles to create a network of paths that are mapped and associated with odometry. Immediately after teaching, the system can drive autonomously within its tracks. This precision lets operators remain confident that the robot will follow a traversable route. However, this operational paradigm has rarely been explored in off-road environments that change significantly through seasonal variation. This paper presents preliminary field trials using lidar and radar implementations of teach and repeat. Using a subset of the data from the upcoming FoMo dataset, we attempted to repeat routes that were 4 days, 44 days, and 113 days old. Lidar teach and repeat demonstrated a stronger ability to localize when the ground points were removed. FMCW radar was often able to localize on older maps, but only with small deviations from the taught path. Additionally, we highlight specific cases where radar localization failed with recent maps due to the high pitch or roll of the vehicle. We highlight lessons learned during the field deployment and highlight areas to improve to achieve reliable teach and repeat with seasonal changes in the environment. Please follow the dataset at https://norlab-ulaval.github.io/FoMo-website for updates and information on the data release.