FoMo is a multi-season collection recorded in a boreal forest environment, featuring deep snow, off-road terrain, steep slopes, and highly variable weather. It provides synchronized multi-modal sensor data, including two lidars (RoboSense and Leishen), an FMCW radar (Navtech), stereo and monocular cameras, dual IMUs, wheel odometry, power data, calibration sequences, and precise ground-truth trajectories via GNSS-PPK fusion.
Designed to support research on robust robot autonomy under adverse conditions, FoMo includes repeated traversals of six trajectories of varying complexity for long-term SLAM and odometry evaluation, as well as rich metadata such as one-minute weather station measurements. The collection is intended to challenge state-of-the-art SLAM, localization, traversability analysis, and multi-season robotics research.
The data is available on AWS. Visit our website for download instructions and more information.