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

Abstract

SLAM (Simultaneous Localization and Mapping) is an efficient method for robot to percept surrendings and make decisions, especially for robots in agricultural scenarios. Perception and path planning in an automatic way is crucial for precision agriculture. However, there are limited public datasets to implement and develop robotic algorithms for agricultural environments. Therefore, we collected dataset “GrapeSLAM”. The ``GrapeSLAM'' dataset comprises video data collected from vineyards to support agricultural robotics research. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing videos under different illumination conditions, and (2) trajectories of the UAV during each flight collected by RTK and IMU. The UAV used was Phantom 4 RTK, equipped with a high resolution camera, flying at around 1 to 3 meters above ground level.