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 region while subject to large variations of meteorological conditions. 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 18.6 km of autonomous navigation in the teach-and-repeat mode. 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 corridors. 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 lessons learned and challenges from our field campaign to support better experimental work in winter conditions.

翻译：森林自动冬季航行固有的固有挑战包括缺乏可靠的全球导航卫星系统信号、低地特征对比、高光度变化和环境变化。这种离地环境是北部地区汽车可能遇到的极端情况。因此,必须了解这种恶劣环境对自主导航系统的影响。为此,我们提交一份实地报告,分析亚北极地区教学和再造导航,同时受气象条件的巨大变化影响。首先,我们描述该系统,该系统依靠点云登记,通过北冰洋森林将移动机器人本地化,同时绘制地图。我们实验性地评估了在18.6公里以上的教学和再生模式下自主导航系统。我们表明,密集的植被会干扰全球导航卫星系统信号,使其不适合在森林轨迹上航行。此外,我们强调在森林走廊使用点云登记使地方化的不确定性增加。我们证明,影响我们系统在环境内地方化能力的不是降雪,而是积雪。最后,我们揭露了我们实地运动的一些经验教训和挑战,以更好地支持冬季实验工作。