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Related Experiment Video

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High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot
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FieldSAFE: Dataset for Obstacle Detection in Agriculture.

Mikkel Fly Kragh1, Peter Christiansen2, Morten Stigaard Laursen3

  • 1Department of Engineering, Aarhus University, Aarhus N 8200, Denmark. mkha@eng.au.dk.

Sensors (Basel, Switzerland)
|November 10, 2017
PubMed
Summary

This study introduces a new multi-modal dataset for agricultural obstacle detection. It features diverse sensors on a tractor, aiding autonomous navigation systems in identifying various static and moving obstacles.

Keywords:
LiDARagriculturecamerascomputer visiondatasetobject trackingobstacle detectionradarstereo imagingthermal imaging

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Area of Science:

  • Agricultural robotics
  • Computer vision
  • Sensor fusion

Background:

  • Obstacle detection is crucial for autonomous agricultural machinery safety and efficiency.
  • Existing datasets may not cover the full range of agricultural scenarios and sensor modalities.

Purpose of the Study:

  • To introduce a comprehensive multi-modal dataset for agricultural obstacle detection.
  • To provide a valuable resource for developing and evaluating autonomous systems in agriculture.

Main Methods:

  • Collected approximately 2 hours of raw sensor data from a tractor-mounted system.
  • Utilized multiple sensing modalities: stereo, thermal, web, 360° cameras, LiDAR, and radar.
  • Integrated precise localization using fused Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS) data.

Main Results:

  • The dataset includes diverse static and moving obstacles such as humans, vehicles, and vegetation.
  • All detected obstacles are annotated with ground truth object labels and precise geographic coordinates.
  • The data was collected in a grass mowing scenario in Denmark.

Conclusions:

  • The presented dataset offers a rich resource for advancing agricultural robotics and autonomous systems.
  • It facilitates research in sensor fusion, object recognition, and localization for agricultural applications.