  Images captured under extremely low light conditions are noise-limited, which
can cause existing robotic vision algorithms to fail. In this paper we develop
an image processing technique for aiding 3D reconstruction from images acquired
in low light conditions. Our technique, based on burst photography, uses direct
methods for image registration within bursts of short exposure time images to
improve the robustness and accuracy of feature-based structure-from-motion
(SfM). We demonstrate improved SfM performance in challenging light-constrained
scenes, including quantitative evaluations that show improved feature
performance and camera pose estimates. Additionally, we show that our method
converges more frequently to correct reconstructions than the state-of-the-art.
Our method is a significant step towards allowing robots to operate in low
light conditions, with potential applications to robots operating in
environments such as underground mines and night time operation.
