亜寒帯森林におけるキロメートル規模の自律ナビゲーション：課題と教訓
Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned
積雪や降雨が変動する亜寒帯森林で、点群レジストレーションによる教示・再現型の自律走行を18.8km以上にわたり実験し、積雪が局所化精度に与える影響やGNSSの限界を明らかにした。
著者: Dominic Baril, Simon-Pierre Deschênes, Olivier Gamache, Maxime Vaidis, Damien LaRocque, Johann Laconte, Vladimír Kubelka, Philippe Giguère, François Pomerleau
分類: cs.RO
原文アブストラクト
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.
