Improving resolution in deep learning-based estimation of drone position and direction using 3D maps

We propose a method to improve the resolution of drone position and direction estimation on the basis of deep learning using three-dimensional (3D) topographic maps in non-global positioning system (GPS) environments. GPS is typically used to estimate the position of drones flying outdoors. However, it becomes difficult to estimate the position if the signal from GPS satellites is blocked by tall mountains or buildings, or if there are interference signals. To avoid this loss of GPS, we previously developed a learning-based flight area estimation method using 3D topographic maps. With this method, the flight area could be estimated with an accuracy of 98.4% in experiments conducted in 25 areas, each 40 meters square. However, a resolution of 40 meters square is difficult to use for drone control. Therefore, in this study, we will verify whether it is possible to improve the resolution by multiplexing the area division and the data acquisition direction. We also investigated whether the flight direction of the drone can be detected using a 3D map. Experimental results show that the position estimation was 96.8% accurate at a resolution of 25 meters square, and the direction estimation was 92.6% accurate for 12-direction estimation.