  Robots operating at night using conventional vision cameras face significant
challenges in reconstruction due to noise-limited images. Previous work has
demonstrated that burst-imaging techniques can be used to partially overcome
this issue. In this paper, we develop a novel feature detector that operates
directly on image bursts that enhances vision-based reconstruction under
extremely low-light conditions. Our approach finds keypoints with well-defined
scale and apparent motion within each burst by jointly searching in a
multi-scale and multi-motion space. Because we describe these features at a
stage where the images have higher signal-to-noise ratio, the detected features
are more accurate than the state-of-the-art on conventional noisy images and
burst-merged images and exhibit high precision, recall, and matching
performance. We show improved feature performance and camera pose estimates and
demonstrate improved structure-from-motion performance using our feature
detector in challenging light-constrained scenes. Our feature finder provides a
significant step towards robots operating in low-light scenarios and
applications including night-time operations.
