Color marker detection with various imaging conditions and occlusion for UAV automatic landing control

Detection of markers for fixed-wing unmanned aerial vehicles play a crucial role in finding a runway to land, automatically. This is because the vehicles cannot land in limited area like rotor-wing UAV. Landing with the fixed-wing need to have a runway that is long and has a lot of symbols for demonstrating the landing point or touch down point. On the other hand, markers are difficult to be searched for, owing to having uncontrollable variables: illumination conditions, diverse environment and object occlusion. Moreover, the number of symbols on runway is another challenging issue. The aircraft controlled by autopilot that is at a height of 100 meters, e.g., may not be able to capture the markers properly before landing. Thus, it cannot land suitably. In order to reduce the complexity of the runway, four circular color markers are utilized to be a simple set of markers for the runway. The number can be increased to 6, 8, etc. for runway length expansion. Our proposed procedure is then: After normalized RGB colors of runway images to alleviate illumination error, detecting markers by Hough circular transform can be searched for even with occlusion. Experimental result shows around 72 to 87 percent accuracy tested by capturing in different scenarios: several exposures, gradations of tone, lens flares, motion blurs and uniform noise as well as object occlusion.