# 森林中的一年：分析亚寒带环境中自主导航的挑战

> 原文链接：https://robotics.hellobike.com/papers/2026-08-27-2608-27628
> 频道：papers | 更新日期：2026-08-27 | 标签：arxiv, cs.RO
> 核心摘要：针对亚寒带森林自主导航难题，论文用一年移动机器人外场数据评估九种方法，并分析季节变化下表现。

# 森林中的一年：分析亚寒带环境中自主导航的挑战

::: v-pre
> 原文链接：[One year in a forest: Analyzing the challenges of autonomous navigation in subarctic environments](https://arxiv.org/abs/2608.27628)（arXiv 2608.27628 · 发布日期：2026-08-27）

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

针对亚寒带森林自主导航难题，论文用一年移动机器人外场数据评估九种方法，并分析季节变化下表现。

## 核心要点

- 亚寒带森林树冠和大气衰减使 GNSS 与云计算不可靠，需要依赖机载感知与处理。
- 研究基于移动机器人一年部署，评估64 km数据上的九种里程计、定位与建图方法。
- 季节变化、自相似场景和高雪堤会削弱先进方法，复杂 SLAM 精度增益有限且更脆弱。
- 跨季节先验地图定位中，激光雷达方法完成定位，雷达和视觉方法因匹配特征少而易失败。

**验证范围与局限**：验证范围为一台移动机器人在亚寒带北方森林的一年外场部署、64 km数据和多季节 T&R 评估。

## 英文摘要 (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.

作者：Matěj Boxan, Nicolas Lauzon, Veronica Vannini, Mathis Turgeon-Roy, François Pomerleau
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## 原始参考来源 (Sources)

- [arXiv 2608.27628：One year in a forest: Analyzing the challenges of autonomous navigation in subarctic environments](https://arxiv.org/abs/2608.27628)