A real-time simple light detection application for a flying robot in extreme noise and interference

In this work, a real-time vision-based algorithm has been developed and implemented on a flying robot, in order to detect and identify a light beacon in the presence of excessive colored noise and interference. Starting from very basic and simple image analysis techniques including color histograms, filtering techniques, and color space analyses, typical pixel-based characteristics or a model of the light beacon has been progressively established. It has been found that not only are various color space-based characteristics significant, but also the relationships between various channels across different color spaces are of great consequence, in a beacon detection problem, specifically referring to a blue light-emitting diode. A block-based search algorithm comprising of multiple thresholds and linear confidence level calculation has been implemented to search established model characteristics in real-time video image data. During implementation, once excessive noise was encountered during flight tests, a simple and low cost noise and interference filter was developed. This filter very effectively handled all noise encountered in real time. The proposed work was successfully implemented and utilized on GeorgiaTech's participating aircraft for the International Aerial Robotics Competition by Association for Unmanned Vehicle Systems International for detection of a blue light-emitting diode problem. Major contributions of this work include establishing a multiple threshold search and detection algorithm based on not only various color channels but also their relationships and handling of as much as 40% noisy or interfered video data with successful practical implementation and demonstration of proposed approach.