Experimental Monitoring of Sea Ice Using Unmanned Aerial Systems

Abstract

The exploration of arctic seas for offshore oil- and gas resources has received increasing interest the past few years. Despite the recent dramatic fall in oil prices, estimates indicate that as much as 22% of the worlds remaining hydrocarbons are located in arctic areas. Thus it is unlikely that the arctic areas will go largely untouched the following decades. One of the main challenges of extracting hydrocarbons in arctic areas is the abundance of sea ice that can cause damaging loads on installations. An important part of oil exploration in these areas is thus the ability to manage potentially damaging sea ice. The current methods for ice management include manned helicopters and other aircraft for detection together with ships to break up or drag away dangerous ice. The main objective of this thesis is to assess the use of Unmanned Aerial Systems (UAS) to perform ice monitoring. An autonomous Unmanned Aerial System for ice detection and mapping using a thermal imaging sensor on a small fixed wing aircraft is proposed. The main contributions of this thesis is a real-time Bayesian recursive algorithm for occupancy grid map estimation representing sea ice. An expedition to Svalbard with several PhD and master students from NTNU was originally planned in April 2015, but this was canceled in March due to time constraints among the participants. The expedition was a major source of inspiration for the methods developed, and an indoor laboratory environment for on-board computer vision was developed using the Robot Operating System (ROS) software framework. The setup included a quadcopter with an on-board camera, and a motion capture system capable of tracking the pose of the quadcopter at 120 Hz. The laboratory setup was used to test much of the planned functionality for the Svalbard expedition. The developed computer vision based map estimation algorithm is capable of running in real time on an on-board computer. As a part of the preparation for the Svalbard excursion, a path planning framework developed by PhD student Anders Albert was successfully tested in the laboratory setup. The experimental results of the mapping algorithm were visually appealing, but closer investigation revealed unsatisfactory accuracy. Using on-board navigational systems alone to perform real-time mapping did not yield sucient accuracy for practical use. Sources of error and means to improve the results in further work were investigated.