发表机构
Université Laval（拉瓦尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过对亚北极北方森林中移动机器人为期一年的部署试验，评估了9种导航方法的性能，发现环境变化会阻碍现有技术，激光雷达跨季节定位表现优于雷达和视觉，还总结了相关挑战与经验。
AI 中文摘要
亚北极地区有望增加自主机器人的部署，应用领域包括林业、采矿和环境监测。在这些条件下，自主系统对GNSS（全球导航卫星系统）或云计算的依赖是不稳定的，因为茂密的树冠和大气衰减会带来影响，因此需要依赖机载传感和数据处理。然而，已有的外部感知模态，包括相机、激光雷达（lidar）和雷达，通常是在结构化城市环境或缺乏显著季节变化的环境中进行评估的。为解决这一问题，本文提供了一份关于移动机器人在亚北极北方森林中为期一年部署的实地报告。我们评估了9种里程计、定位和建图方法的64公里数据，并评估了它们在季节变化中的性能。实验表明，环境变化会显著阻碍最先进技术的性能，这些技术在面临自相似场景或高大雪堆等条件时会表现出更高的脆弱性。此外，复杂的同步定位与建图（SLAM）算法相比本体感知基线方法，仅能提供有限的精度提升，同时会显著增加系统的脆弱性。通过将位置漂移与特征和置信度权重分布相关联，我们表明基于视觉的SLAM方法受季节变化的影响尤为严重。此外，我们研究了先验地图中的跨季节定位任务。基于激光雷达的方法成功完成了季节间的定位运行，而雷达和视觉方法则容易因不同运行之间匹配特征较少而失败，即使在同一季节内也是如此。最后，我们详细介绍了从这一年的试验中获得的挑战和经验教训，包括使用基于雷达和激光雷达的管道进行的多季节示教与重复（Teach and Repeat，T&R）评估。
英文摘要
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.
