A Method of 2D Semantic Map Generation for Autonomous Flight of MAVs

In many applications, MAVs(Micro Aerial Vehicle) need to recognize the ground targets and locate them precisely, or in other words, to generate a semantic map of those targets, to guide the MAVs to complete mission automatically. In this paper, we introduce a method of fast 2D semantic map generation using only the images captured by the downward-looking camera in an unknown environment. The map is firstly stitched from each individual image by feature matching. Then the contour and area of each target are extracted through color detection in the global map. Finally, neural network is utilized for recognition of ground targets marked by different printed numbers. We have tested our method for automatic task performing as an MAV competition challenge in a virtual environment. By taking the generated 2D semantic map into the control loop, the MAV can localize itself, realize autonomous flight, detect and explore the environment.