Deriving a priori environment information for ground-based exploration using aerial observations

Activity: Talk or presentation / LecturePresentation at a scientific conference / workshop

Description

Autonomous Mobile Robots (AMRs) are emerging as a potential solution for transitioning agriculture towards sustainability by optimizing resource utilization in applications such as water and fertilizer use. However, deploying AMRs requires a map of the environment for effective operation. This study investigates UAVs equipped with RGB cameras to derive such a map that can be used by ground-level AMRs in vineyards. Our approach includes capturing detailed and frequent aerial imagery to reflect the vineyard’s condition, identifying obstacles and separating vine growth from other vegetation through terrain model-based segmentation, and refining segmentation through vine-row reconstruction to enhance the accuracy and utility of the map for navigation. We compare our approach on three flights on a local vineyard with manually annotated ground truth data and show that our approach is a viable solution to derive an a priori map for autonomous ground vehicles in vineyards.
Period25 Mar 202427 Mar 2024
Event titleThe First Austrian Symposium on AI, Robotics, and Vision
Event typeConference
LocationInnsbruck, AustriaShow on map
Degree of RecognitionNational

Research Field

  • Assistive and Autonomous Systems

Keywords

  • Autonomous Driving
  • vine-row reconstruction
  • mapping