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Related Concept Videos

  1. Home
  3. Research Domains
  5. Agricultural, Veterinary And Food Sciences
  7. Agriculture, Land And Farm Management
  9. Agricultural Production Systems Simulation
  11. Grapeslam: Uav-based Monocular Visual Dataset For Slam, Sfm And 3d Reconstruction With Trajectories Under Challenging Illumination Conditions

GrapeSLAM: UAV-based monocular visual dataset for SLAM, SfM and 3D reconstruction with trajectories under challenging illumination conditions

Kaiwen Wang1,2, Sergio Vélez3, Lammert Kooistra4

  • 1Information Technology Group, Wageningen University & Research, Wageningen, 6708 PB, the Netherlands.

Data in Brief
|April 15, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces GrapeSLAM, a new dataset for agricultural robotics. It provides crucial video data from vineyards to advance robot perception and path planning in precision agriculture.

Area of Science:

  • Robotics
  • Computer Vision
  • Agricultural Science

Background:

  • Simultaneous Localization and Mapping (SLAM) is vital for robot navigation and decision-making in agriculture.
  • Precision agriculture requires automated perception and path planning for efficiency.
  • A lack of public datasets hinders the development of agricultural robotics algorithms.

Purpose of the Study:

  • To introduce the "GrapeSLAM" dataset, a novel resource for agricultural robotics research.
  • To provide comprehensive video data from vineyard environments.
  • To facilitate the development and testing of SLAM algorithms in agriculture.

Main Methods:

  • Collected vineyard video data using a Phantom 4 RTK unmanned aerial vehicle (UAV).
  • Captured data under varying illumination conditions at 1-3 meters above ground level.
Keywords:
Precision agricultureRTK GNSS dataUAVVisual SLAM

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  • Recorded UAV trajectories using RTK and IMU for accurate localization.

    • Main Results:

    • The "GrapeSLAM" dataset offers diverse video footage from vineyard settings.
    • Includes precise trajectory data synchronized with visual information.
    • Enables robust testing of robotic perception and navigation systems.

    Conclusions:

    • The "GrapeSLAM" dataset addresses the need for specialized agricultural robotics data.
    • It supports advancements in autonomous navigation and perception for vineyard operations.
    • Facilitates research in precision agriculture through accessible, high-quality data.
    Woody crop