FoMo: Multi-Season Robot Navigation Data from a Boreal Forest
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Description
A multi-season dataset for robot navigation recorded in the boreal forest environment of Forêt Montmorency. It provides synchronized multi-modal sensor data from two lidars, an FMCW radar, stereo and monocular cameras, dual IMUs, wheel odometry, power data, and precise ground-truth trajectories via GNSS-PPK fusion. The dataset was created by Norlab at Université Laval and is hosted on AWS Open Data.
Use Cases
Evaluate long-term SLAM and odometry performance based on repeated traversals of six trajectories.
Develop traversability analysis models based on multi-season lidar, radar, and camera data in challenging terrain.
Benchmark multi-sensor fusion for localization based on synchronized lidar, radar, camera, and IMU streams.
Study the impact of variable weather on robot navigation based on integrated one-minute weather station measurements.
Strengths
Includes synchronized data from multiple sensor types: two lidars, an FMCW radar, stereo and monocular cameras, and dual IMUs.
Provides precise ground-truth trajectories generated via GNSS-PPK fusion.
Contains repeated traversals of six trajectories of varying complexity for longitudinal evaluation.
Features rich metadata including one-minute weather station measurements.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count and total dataset size are unknown, which may limit suitability assessment.
Last update date is unknown; freshness unverified.
Provenance
Source
Norlab, Université Laval
Collection Method
Recorded via robot platform in Forêt Montmorency.
Time Range
Multi-season coverage, but specific dates are unknown.
Freshness
Last updated date is unknown.
Geography
Forêt Montmorency, a boreal forest environment.
Data is hosted in S3 format on AWS Open Data and is licensed under CC-BY-4.0.