Vision-based Autonomous Tracking of High-Rise Vertical Structure using a Quadcopter

Inspection of the high-rise vertical structure is one of the most important and difficult tasks in industries for continuous operation. The majority of industrial subsurface utilities are manually examined, which takes a lot of time, money, and planning. Robot-based autonomous navigation and inspection can be a potential solution to this problem. An autonomous drone that can autonomously navigate around the structure at a high speed and has a significant payload capacity can improve the inspection quality and can perform at the minimum time with a high frequency of inspection. This research paper focuses on developing vision-based autonomous navigation and motion control algorithms for quadcopters to track high-rise industrial vertical structures. For the detection of the vertical structure, we have employed a CNN-based image segmentation model, and for the motion control for navigation around the structure, a linear control algorithm has been used. This paper discusses all the results related to the positioning and motion control of the drone, the robot vision algorithm, and the two distinct paths that the quadcopter has followed.