FoMo - A Multi-Season Dataset for Robot Navigation in Forêt Montmorency

The FoMo dataset 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 ...

FoMo数据集是一套采集于北方针叶林环境的多季候实测数据集，其场景涵盖深厚积雪、越野地形、陡坡以及高度多变的天气条件。该数据集提供同步多模态传感器数据，包括RoboSense与Leishen两款激光雷达（LiDAR）、一台调频连续波（FMCW）雷达（Navtech）、立体与单目相机、双惯性测量单元（IMU）、轮式里程计、电力数据、标定序列，以及通过GNSS-PPK融合得到的高精度地面真值轨迹。本数据集旨在支撑恶劣环境下机器人自主系统鲁棒性相关研究，FoMo包含六条复杂度各异的轨迹的重复遍历数据，用于……