FoMo: A Multi-Season Dataset for Robot Navigation in For\^et Montmorency

2026-03-09

AI Summary

This study addresses the challenge of robotic localization and mapping failure in boreal forests due to dramatic seasonal changes—including snow cover, vegetation occlusion, and varying ground traction—by conducting a year-long multi-season field campaign at Forêt Montmorency in Quebec. The resulting high-challenge, multimodal navigation dataset comprises 64 km of trajectories across 12 repeated deployments, uniquely spanning a full annual cycle with extreme seasonal transitions and complex terrain. It integrates rotating and solid-state LiDARs, FMCW radar, stereo and wide-angle cameras, dual IMUs, multi-constellation GNSS, and post-processed static base station positioning, complemented by rich metadata on weather conditions and power consumption. Preliminary evaluations reveal significant degradation in relocalization performance of existing LiDAR-inertial, radar-gyro, and visual-inertial methods across seasons, underscoring the dataset’s value in benchmarking the robustness of long-term autonomous navigation algorithms.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance

Abstract

The For\^et Montmorency (FoMo) dataset is a comprehensive multi-season data collection, recorded over the span of one year in a boreal forest. Featuring a unique combination of on- and off-pavement environments with significant environmental changes, the dataset challenges established odometry and SLAM pipelines. Some highlights of the data include the accumulation of snow exceeding 1 m, significant vegetation growth in front of sensors, and operations at the traction limits of the platform. In total, the FoMo dataset includes over 64 km of six diverse trajectories, repeated during 12 deployments throughout the year. The dataset features data from one rotating and one hybrid solid-state lidar, a Frequency Modulated Continuous Wave (FMCW) radar, full-HD images from a stereo camera and a wide lens monocular camera, as well as data from two IMUs. Ground Truth is calculated by post-processing three GNSS receivers mounted on the Uncrewed Ground Vehicle (UGV) and a static GNSS base station. Additional metadata, such as one measurement per minute from an on-site weather station, camera calibration intrinsics, and vehicle power consumption, is available for all sequences. To highlight the relevance of the dataset, we performed a preliminary evaluation of the robustness of a lidar-inertial, radar-gyro, and a visual-inertial localization and mapping techniques to seasonal changes. We show that seasonal changes have serious effects on the re-localization capabilities of the state-of-the-art methods. The dataset and development kit are available at https://fomo.norlab.ulaval.ca.

Problem

• seasonal changes
• robot navigation
• SLAM
• odometry
• boreal forest

Innovation

• multi-season dataset
• boreal forest navigation
• sensor fusion
• seasonal robustness
• SLAM benchmark