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This field study reveals that standard autonomous navigation techniques struggle significantly in subarctic forests due to seasonal variations, dense canopies, and snowbanks. The research demonstrates that complex SLAM algorithms often introduce fragility without providing substantial accuracy gains over simpler proprioceptive baselines in these harsh conditions. For the AI community, this highlights the critical need for robust onboard sensing and data processing strategies that do not rely on GNSS or cloud connectivity in extreme environments.
One year in a forest: Analyzing the challenges of autonomous navigation in subarctic environments
arXiv:2608.27628v1 Announce Type: new Abstract: Subarctic regions have the potential to see increased deployment of autonomous robots in applications including forestry, mining, and environmental monitoring. In these conditions, an autonomous system's reliance on GNSS or cloud computing is precarious due to dense tree canopies and atmospheric attenuation, necessitating onboard sensing and data processing. However, established exteroceptive modalities, including cameras, lidars, and radars, are typically evaluated in structured urban settings or in environments that lack significant seasonal variations. To address this, we present a field report on a year-long deployment of a mobile robot in a subarctic boreal forest. We evaluate 64 km of data using nine odometry, localization, and mapping methods and assess their performance across seasonal changes. The performed experiments suggest that the environment changes significantly hinder the performance of state-of-the-art techniques, which show increased fragility when subject to conditions characterized by self-similar scenes or tall snowbanks. Additionally, complex Simultaneous Localization and Mapping (SLAM) algorithms offer limited accuracy gains over a proprioceptive baseline while significantly increasing system fragility. Furthermore, by correlating the position drift with features and confidence weight distribution, we show that visual-based SLAM methods are particularly affected by the seasonal changes. Additionally, we investigate the task of cross-season localization in a prior map. While lidar-based methods successfully completed localization runs between seasons, radar and visual methods are prone to failure due to a few matching features between runs, even within the same season. Finally, we detail the challenges and lessons learned from this year-long trial, including a multi-season Teach and Repeat (T&R) evaluation using both radar and lidar-based pipelines.
Abstract:Subarctic regions have the potential to see increased deployment of autonomous robots in applications including forestry, mining, and environmental monitoring. In these conditions, an autonomous system's reliance on GNSS or cloud computing is precarious due to dense tree canopies and atmospheric attenuation, necessitating onboard sensing and data processing. However, established exteroceptive modalities, including cameras, lidars, and radars, are typically evaluated in structured urban settings or in environments that lack significant seasonal variations. To address this, we present a field report on a year-long deployment of a mobile robot in a subarctic boreal forest. We evaluate 64 km of data using nine odometry, localization, and mapping methods and assess their performance across seasonal changes. The performed experiments suggest that the environment changes significantly hinder the performance of state-of-the-art techniques, which show increased fragility when subject to conditions characterized by self-similar scenes or tall snowbanks. Additionally, complex Simultaneous Localization and Mapping (SLAM) algorithms offer limited accuracy gains over a proprioceptive baseline while significantly increasing system fragility. Furthermore, by correlating the position drift with features and confidence weight distribution, we show that visual-based SLAM methods are particularly affected by the seasonal changes. Additionally, we investigate the task of cross-season localization in a prior map. While lidar-based methods successfully completed localization runs between seasons, radar and visual methods are prone to failure due to a few matching features between runs, even within the same season. Finally, we detail the challenges and lessons learned from this year-long trial, including a multi-season Teach and Repeat (T&R) evaluation using both radar and lidar-based pipelines.
Robotics (cs.RO)
arXiv:2608.27628 [cs.RO]
(or arXiv:2608.27628v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.27628 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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