One year in a forest: Analyzing the challenges of autonomous navigation in subarctic environments

• URL: http://arxiv.org/abs/2608.27628v1
• Date: Thu, 27 Aug 2026 19:10:10 GMT
• Title: One year in a forest: Analyzing the challenges of autonomous navigation in subarctic environments
• Abstract summary: Subarctic regions have the potential to see increased deployment of autonomous robots in applications including forestry, mining, and environmental monitoring.<n>We present a field report on a year-long deployment of a mobile robot in a subarctic boreal forest.<n>We evaluate 64 km of data using nine odometry, localization, and mapping methods and assess their performance across seasonal changes.
• Score: 1.7846146419522955
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Perception-Aware Autonomous Exploration in Feature-Limited Environments [9.046854275196605]
  We propose a hierarchical perception-aware exploration framework for a stereo-equipped unmanned aerial vehicle (UAV)<n>Our approach associates each candidate frontier with an expected feature quality using a global feature map, and prioritises visually informative subgoals.<n>Compared to baselines that ignore feature quality and/or do not optimise continuous yaw, our method maintains more reliable feature tracking, reduces odometry drift, and on average 30% higher coverage before the odometry error exceeds specified thresholds.
  arXiv  Detail & Related papers  (2026-03-16T17:55:56Z)
• VinePT-Map: Pole-Trunk Semantic Mapping for Resilient Autonomous Robotics in Vineyards [40.46631989657389]
  This paper introduces VinePT-Map, a semantic mapping framework that leverages vine trunks and support poles as persistent structural landmarks.<n>The proposed method formulates the mapping problem as a factor graph, integrating GPS, IMU, and RGB-D observations through robust geometrical constraints.<n>An efficient perception pipeline based on instance segmentation and tracking, combined with a clustering filter for outlier rejection and pose refinement, enables accurate landmark detection.
  arXiv  Detail & Related papers  (2026-03-05T11:39:12Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather [48.65180004211851]
  We present a robust monocular depth estimation method called textbfACDepth from the perspective of high-quality training data generation and domain adaptation.<n>Specifically, we introduce a one-step diffusion model for generating samples that simulate adverse weather conditions, constructing a multi-tuple degradation dataset during training.<n>We elaborate on a multi-granularity knowledge distillation strategy (MKD) that encourages the student network to absorb knowledge from both the teacher model and pretrained Depth Anything V2.
  arXiv  Detail & Related papers  (2025-05-18T02:30:47Z)
• SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping [13.471629436653249]
  This work presents SHAZAM - Self-Supervised Change Monitoring for Hazard Detection and Mapping.<n>SHAZAM uses a lightweight conditional UNet to generate expected images of a region of interest for any day of the year.<n>A modified structural similarity measure compares the generated images with actual satellite observations to compute region-level anomaly scores and pixel-level hazard maps.
  arXiv  Detail & Related papers  (2025-03-01T04:45:46Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• 4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions [46.03430162297781]
  We present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset.<n>The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions.<n>We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance.
  arXiv  Detail & Related papers  (2022-12-31T13:52:36Z)
• 6D Camera Relocalization in Visually Ambiguous Extreme Environments [79.68352435957266]
  We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Our method achieves comparable performance with state-of-the-art methods on the indoor benchmark (7-Scenes dataset) using only 20% training data.
  arXiv  Detail & Related papers  (2022-07-13T16:40:02Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.