FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h 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∘ 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.

[1]  Herman Herman,et al.  A System for Semi-Autonomous Tractor Operations , 2002, Auton. Robots.

[2]  Hans W. Griepentrog,et al.  Safe and Reliable - Further Development of a Field Robot , 2009 .

[3]  Giulio Reina,et al.  Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision , 2012, Sensors.

[4]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[5]  Deva Ramanan,et al.  Efficiently Scaling up Crowdsourced Video Annotation , 2012, International Journal of Computer Vision.

[6]  David Ball,et al.  Vision‐based Obstacle Detection and Navigation for an Agricultural Robot , 2016, J. Field Robotics.

[7]  Carlos Vallespi,et al.  AUTOMATING ORCHARDS: A SYSTEM OF AUTONOMOUS TRACTORS FOR ORCHARD MAINTENANCE , 2012 .

[8]  Michael Felsberg,et al.  A Multi-sensor Traffic Scene Dataset with Omnidirectional Video , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[9]  Jiri Matas,et al.  A system for real-time detection and tracking of vehicles from a single car-mounted camera , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[10]  Giulio Reina,et al.  Ambient awareness for agricultural robotic vehicles , 2016, ArXiv.

[11]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Paul Newman,et al.  1 year, 1000 km: The Oxford RobotCar dataset , 2017, Int. J. Robotics Res..

[13]  R. N. Jørgensen,et al.  Platform for evaluating sensors and human detection in autonomous mowing operations , 2017, Precision Agriculture.

[14]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[15]  Antonio M. López,et al.  The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Noah Snavely,et al.  NYC3DCars: A Dataset of 3D Vehicles in Geographic Context , 2013, 2013 IEEE International Conference on Computer Vision.

[17]  Aaron C. Courville,et al.  Interacting Markov Random Fields for Simultaneous Terrain Modeling and Obstacle Detection , 2005, Robotics: Science and Systems.

[18]  Anthony Stentz,et al.  Vision-based perception for an automated harvester , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[19]  David Ball,et al.  Online novelty-based visual obstacle detection for field robotics , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Qiao Wang,et al.  VirtualWorlds as Proxy for Multi-object Tracking Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Thierry Peynot,et al.  The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging Conditions , 2010, Int. J. Robotics Res..

[22]  Thomas Moore,et al.  A Generalized Extended Kalman Filter Implementation for the Robot Operating System , 2014, IAS.

[23]  D. J. Hills,et al.  Autoguidance system operated at high speed causes almost no tomato damage , 2004 .

[24]  Peter Kontschieder,et al.  The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[25]  Gonzalo Pajares,et al.  New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots , 2014, TheScientificWorldJournal.

[26]  Herman Herman,et al.  Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset , 2017, ArXiv.