Automated navigation of Unmanned Aircraft Systems (UAS) in a broad range of illumination scenarios implies improved and real-time depth estimation and long-distance obstacle detection. We present our lightweight ultra wide-angle camera optimized for low-light illumination (down to < 1 lux) mounted on a drone and compare its optical performance with other module found in the market. We also capture images from the drone in flight and test them on monocular depth estimation neural networks and show that our camera module is suitable for low-light navigation.
The next generation of sUAS (small Unmanned Aircraft Systems) for automated navigation will have to perform in challenging conditions, bad weather, high and low temperature and from dusk-to-dawn. The paper presents experimental results from a new wide-angle vision camera module specially optimized for low-light. We present the optical characteristics of this system as well as experimental results obtained for different sense and avoid functionalities. We also show preliminary results using our camera module images on neural networks for different scene understanding tasks.
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