FoMo Dataset Tutorials

Companion notebooks and scripts for the FoMo (Forêt Montmorency) Multi-Season Robot Navigation Dataset, hosted on the AWS Open Data Registry.

Boxan et al. (2026). FoMo: A Multi-Season Dataset for Robot Navigation in Forêt Montmorency. arXiv:2603.08433 [cs.RO]. https://arxiv.org/abs/2603.08433

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About the Dataset

The FoMo dataset is a year-long robotic data collection recorded in a boreal forest 80 km north of Quebec City, Canada. It features over 64 km of trajectories repeated across 12 deployments spanning dramatic seasonal changes — from −19 °C winters with over 1 m of snow to warm summers with dense vegetation.

Sensors: 2× lidar, 1× FMCW radar, stereo camera, monocular camera, 2× IMU, 2× microphone, GNSS
Ground truth: PPK-GNSS with per-point covariances, TUM format
Storage: s3://fomo-dataset (public, no AWS credentials required)
Website: fomo.norlab.ulaval.ca

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Contents

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├── get_to_know_fomo-dataset.ipynb   # Guided tour notebook (Jupyter)
├── get_to_know_fomo-dataset.py      # Same tutorial as a standalone Python script
└── README.md                # This file

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Get to Know the Dataset

The main tutorial — fomo_get_to_know.ipynb — is a guided tour of the dataset that answers six key questions:

Section	Description
Q1	How is the dataset organized?
Q2	What data formats are present?
Q3	How do I download and load data? (CSV, IMU, lidar, radar, ground truth, calibration)
Q4	Visualizations — trajectory, lidar BEV, radar polar image, seasonal conditions, power consumption
Q5	One answered question: robustness of localization methods to seasonal changes
Q6	One open challenge: terrain traversability prediction across seasons

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Quick Start

Requirements

pip install boto3 pandas numpy matplotlib Pillow

No AWS account is required — the dataset is publicly accessible.

Run as a Jupyter notebook

jupyter notebook fomo_get_to_know.ipynb

Run as a Python script

python fomo_get_to_know.py

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License

The FoMo dataset is publicly available under the license described in s3://fomo-dataset/LICENSE.txt.

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Citation

@misc{Boxan2026_fomo,
  title   = {{FoMo: A Multi-Season Dataset for Robot Navigation in For\^et Montmorency}},
  author  = {Matěj Boxan and Gabriel Jeanson and Alexander Krawciw and Effie Daum
             and Xinyuan Qiao and Sven Lilge and Timothy D. Barfoot and François Pomerleau},
  year    = {2026},
  eprint  = {2603.08433},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url     = {https://arxiv.org/abs/2603.08433}
}