This project (Sept. 2022-Dec. 2024) aims at increasing the navigation autonomy of Search-And-Rescue drones while preserving their energy autonomy. This requires to improve existing Simultaneous Localization And Mapping (SLAM) and obstacle-avoidance algorithms already employed on drones. Towards this goal, we advocate the enhancement of sensing and processing tasks through low-energy hardware such as event cameras and Field-Programmable Gate Arrays (FPGAs) and to design SLAM and obstacle-avoidance algorithms in a way that capitalizes deep neural network (DNNs) architectures that are adapted to this new hardware. This project will prototype such an integrated system that will be made available to the scientific community to allow further investigations on the opportunities brought by this novel concept of drone architecture.