  In this paper, we present a novel multi-modal dataset for obstacle detection
in agriculture. The dataset comprises approximately 2 hours of raw sensor data
from a tractor-mounted sensor system in a grass mowing scenario in Denmark,
October 2016. Sensing modalities include stereo camera, thermal camera, web
camera, 360-degree camera, lidar, and radar, while precise localization is
available from fused IMU and GNSS. Both static and moving obstacles are present
including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and
vegetation. All obstacles have ground truth object labels and geographic
coordinates.
