Error reduction in autonomous multirotor vision-based landing system with helipad context

Drone applications in military and civil fields continue to be of great interest, but all of them require a critical and essential maneuver, landing. Although the problem of autonomous landing is not new, the continuous technological advances in sensorization, navigation systems, or data processing among others, encourage the emergence of new strategies continuously. A widespread approach in the literature for multirotor vehicles are based on computer vision. However, from the simplest system with monocular vision to the most complex with multiple sensors, it can be improved if its behavior is studied. This paper evaluates the global position estimation accuracy of a landing pad provided by an autonomous landing system using information from the aircraft navigation system, the vehicle attitude, and a gimbal-adjustable monocular vision system. The results of this study show that bias correction over a polar space improves a set of quality metrics that evaluate the performance of the landing phase.