# Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned

arXiv: https://arxiv.org/abs/2111.13981 · HTML: https://mlsanity.com/paper/2111.13981/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2021.

## Built on by (most cited first, 6 of 6)

- [Proprioception Is All You Need: Terrain Classification for Boreal Forests](https://mlsanity.com/paper/2403.16877/) (2024)
- [Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learned](https://mlsanity.com/paper/2506.18844/) (2025)
- [DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty](https://mlsanity.com/paper/2506.16593/) (2025)
- [Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation](https://mlsanity.com/paper/2509.17287/) (2025)
- [Drone-Assisted UAV-UGV Collaboration for Autonomous Navigation in Snow-Covered Terrain](https://mlsanity.com/paper/2608.07797/) (2026)
- [Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion](https://mlsanity.com/paper/2610.13872/) (2026)

## Abstract (arXiv)

Challenges inherent to autonomous wintertime navigation in forests include lack of reliable a Global Navigation Satellite System (GNSS) signal, low feature contrast, high illumination variations and changing environment. This type of off-road environment is an extreme case of situations autonomous cars could encounter in northern regions. Thus, it is important to understand the impact of this harsh environment on autonomous navigation systems. To this end, we present a field report analyzing teach-and-repeat navigation in a subarctic forest while subject to fluctuating weather, including light and heavy snow, rain and drizzle. First, we describe the system, which relies on point cloud registration to localize a mobile robot through a boreal forest, while simultaneously building a map. We experimentally evaluate this system in over 18.8 km of autonomous navigation in the teach-and-repeat mode. Over 14 repeat runs, only four manual interventions were required, three of which were due to localization failure and another one caused by battery power outage. We show that dense vegetation perturbs the GNSS signal, rendering it unsuitable for navigation in forest trails. Furthermore, we highlight the increased uncertainty related to localizing using point cloud registration in forest trails. We demonstrate that it is not snow precipitation, but snow accumulation, that affects our system's ability to localize within the environment. Finally, we expose some challenges and lessons learned from our field campaign to support better experimental work in winter conditions. Our dataset is available online.