Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned
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 c...
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ftdatacite:10.48550/arxiv.2111.13981 2023-05-15T18:28:14+02:00 Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned Baril, Dominic Deschênes, Simon-Pierre Gamache, Olivier Vaidis, Maxime LaRocque, Damien Laconte, Johann Kubelka, Vladimír Giguère, Philippe Pomerleau, François 2021 https://dx.doi.org/10.48550/arxiv.2111.13981 https://arxiv.org/abs/2111.13981 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Robotics cs.RO FOS Computer and information sciences Article CreativeWork article Preprint 2021 ftdatacite https://doi.org/10.48550/arxiv.2111.13981 2022-03-10T13:33:19Z 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. : Preprint. Submitted to Field Robotics. 27 pages, 20 figures, 2 tables Article in Journal/Newspaper Subarctic DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Robotics cs.RO FOS Computer and information sciences |
spellingShingle |
Robotics cs.RO FOS Computer and information sciences Baril, Dominic Deschênes, Simon-Pierre Gamache, Olivier Vaidis, Maxime LaRocque, Damien Laconte, Johann Kubelka, Vladimír Giguère, Philippe Pomerleau, François Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned |
topic_facet |
Robotics cs.RO FOS Computer and information sciences |
description |
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. : Preprint. Submitted to Field Robotics. 27 pages, 20 figures, 2 tables |
format |
Article in Journal/Newspaper |
author |
Baril, Dominic Deschênes, Simon-Pierre Gamache, Olivier Vaidis, Maxime LaRocque, Damien Laconte, Johann Kubelka, Vladimír Giguère, Philippe Pomerleau, François |
author_facet |
Baril, Dominic Deschênes, Simon-Pierre Gamache, Olivier Vaidis, Maxime LaRocque, Damien Laconte, Johann Kubelka, Vladimír Giguère, Philippe Pomerleau, François |
author_sort |
Baril, Dominic |
title |
Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned |
title_short |
Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned |
title_full |
Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned |
title_fullStr |
Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned |
title_full_unstemmed |
Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned |
title_sort |
kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2111.13981 https://arxiv.org/abs/2111.13981 |
genre |
Subarctic |
genre_facet |
Subarctic |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2111.13981 |
_version_ |
1766210621986045952 |