Robots operating in low-light conditions with conventional cameras face significant challenges due to the low signal-to-noise ratio in the images. Previous work has demonstrated the use of burst-imaging techniques to partially overcome this issue. This study proposes a novel feature finder that enhances vision-based reconstruction under extremely low-light conditions. The approach locates features with well-defined scale and apparent motion within each burst by jointly searching in a scale-slope space. We demonstrate improved performance in feature detection, camera pose estimation and reconstruction compared to state-of-the-art feature extractors on conventional and burst-merged images. This work opens avenues for robotic applications where low-light conditions often pose difficulties such as disaster recovery and drone delivery at night.