Vision-based control of a quadrotor for an object inspection scenario

Unmanned aerial vehicles (UAVs) have gained special attention in recent years, among others in monitoring and inspection applications. In this paper, a less explored application field is proposed, railway inspection, where UAVs can be used to perform visual inspection tasks such as semaphore, catenary, or track inspection. We focus on lightweight UAVs, which can detect many events in railways (for example missing indicators or cabling, or obstacles on the tracks). An outdoor scenario is developed where a quadrotor visually detects a railway semaphore and flies around it autonomously, recording a video of it for offline post-processing. For these tasks, we exploit object detection methods from literature, and develop a visual servoing technique. Additionally, we perform a thorough comparison of several object detection approaches before selecting a preferred method. Then, we show the performance of the presented filtering solutions when they are used in servoing, and conclude our experiments with evaluating real outdoor flight trajectories using an AR.Drone 2.0 quadrotor.