FoMo: A Proposal for a Multi-Season Dataset for Robot Navigation in Forêt Montmorency
arxiv(2024)
摘要
In this paper, we propose the FoMo (Forêt Montmorency) dataset: a
comprehensive, multi-season data collection. Located in the Montmorency Forest,
Quebec, Canada, our dataset will capture a rich variety of sensory data over
six distinct trajectories totaling 6 kilometers, repeated through different
seasons to accumulate 42 kilometers of recorded data. The boreal forest
environment increases the diversity of datasets for mobile robot navigation.
This proposed dataset will feature a broad array of sensor modalities,
including lidar, radar, and a navigation-grade Inertial Measurement Unit (IMU),
against the backdrop of challenging boreal forest conditions. Notably, the FoMo
dataset will be distinguished by its inclusion of seasonal variations, such as
changes in tree canopy and snow depth up to 2 meters, presenting new challenges
for robot navigation algorithms. Alongside, we will offer a centimeter-level
accurate ground truth, obtained through Post Processed Kinematic (PPK) Global
Navigation Satellite System (GNSS) correction, facilitating precise evaluation
of odometry and localization algorithms. This work aims to spur advancements in
autonomous navigation, enabling the development of robust algorithms capable of
handling the dynamic, unstructured environments characteristic of boreal
forests. With a public odometry and localization leaderboard and a dedicated
software suite, we invite the robotics community to engage with the FoMo
dataset by exploring new frontiers in robot navigation under extreme
environmental variations. We seek feedback from the community based on this
proposal to make the dataset as useful as possible. For further details and
supplementary materials, please visit
https://norlab-ulaval.github.io/FoMo-website/.
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