  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
forest while subject to fluctuating weather, including light and heavy snow,
rain and drizzle. 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
18.8 km of autonomous navigation in the teach-and-repeat mode. Over 14 repeat
runs, only four manual interventions were required, three of which were due to
localization failure and another one caused by battery power outage. 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
trails. 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 challenges and lessons learned from our
field campaign to support better experimental work in winter conditions. Our
dataset is available online.

Robotics