Overcoming the Blind Spot in CNN-based Gate Detection for Autonomous Drone Racing

In recent years Autonomous Drone Racing has become a significant challenge due to the problems involved in developing an algorithm for autonomous navigation. One of the major problems is the estimation of the camera pose; several approaches can be founded to achieve the estimation of the camera pose. In particular, it is possible to estimates the position based on specific object detection. However, object detection at the same time of navigation entails the problem of a blind spot area when the camera is closest to the object. We propose a methodology that overcomes the blind spot in autonomous navigation based on CNN gate detection to perform pose estimation with a stochastic algorithm for distance estimation. We achieve over 95 % in gate detection and a mean error of around 35 cm in 1D pose estimation into the blind spot zone.