Unmanned aircraft systems (UASs) need to perform accurate autonomous navigation using sense-and-avoid algorithms under varying illumination conditions. This requires robust algorithms able to perform consistently, even when image quality is poor. In this presentation we will share the results of our research on the impact of noise and blur on corner detection algorithms and CNN-based 2D object detectors used for drone navigation. Specifically, we will show how to fine-tune these algorithms to make them effective in extreme low light (0.5 lux) and on images with high levels of noise or blur. We will also highlight the main benefits of using such computer vision methods for drone navigation.