Visual Based Navigation for Autonomous Underwater Vehicle

The objective of the project is to research and develop a vision-based system for underwater navigation. The vision system will use new and state-of-the-art computer vision and deep learning. A visual global positioning will be based on a map of selected visual landmarks and a recognizer to detect landmarks. Visual SLAM will measure movement between landmarks and create a point cloud to navigate and avoid collision. A pipeline detector will estimate the relative pose and guide the AUV along the pipeline. Furthermore, visual recognition algorithms must perform automated inspection by detecting events such as anode, joint, debris and damage on the pipeline.

Project facts

  • Period: 2018-2020
  • Company: EIVA
  • Industrial Postdoc: Peter Christiansen
  • University Mentor: Henrik Karstoft
  • Innovation Fund Denmark investment: 0.888 MDKK
  • Budget: 1.571 MDKK