Analysis of Computer Vision-Based Techniques for the Recognition of Landing Platforms for UAVs

Although advanced UAVs use self-guided landing with the support of GPS devices, issues such as signal loss, support for indoor environments, accuracy issues, etc., must be considered, which contribute greatly to the overall success of autonomous flight. Therefore, this chapter proposes two different methods that allow for the recognition of a landing platform using Computer Vision techniques in order to assist autonomous landing. The first method is based on an Expert System that allows for the recognition of a patented black and white platform by performing a geometric analysis of the regions based on thresholds that allow for a degree of plane distortion. The second method, based on Cognitive Computing, can be used to solve the limitations to plane distortion inherent to the first approach, and further uses a specific landing platform with six different colors in order to combine color segmentation techniques with pattern recognition algorithms, together with an intelligent geometric analysis based on a decision-making technique. As a result, recognition can be achieved at different ranges and inclination angles between the vision system and the platform. It is not affected by distortions to the image figures due to perspective projection, even making it possible to perform the recognition with only a partial view of the platform, something that has received scant attention in the literature to date. The novelty is therefore the robustness and precision in the recognition from a wide variety of perspectives, different lighting conditions, and even problems that result in only a partial view of the platform, such as those resulting from partial focus or blind spots due to overexposure.