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arXiv · 1709.03526

FieldSAFE: Dataset for Obstacle Detection in Agriculture

Mikkel Fly Kragh·Peter Christiansen·Morten Stigaard Laursen·Morten Larsen·Kim Arild Steen·Ole Green·Henrik Karstoft·Rasmus Nyholm Jørgensen
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Abstract

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.

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Mikkel Fly Kragh, Peter Christiansen, Morten Stigaard Laursen, Morten Larsen, Kim Arild Steen, Ole Green, Henrik Karstoft, Rasmus Nyholm Jørgensen. 2017-09-11. FieldSAFE: Dataset for Obstacle Detection in Agriculture. https://doi.org/10.3390/s17112579

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