Learning visual feature descriptors for dynamic lighting conditions

In many robotic applications, especially long-term outdoor deployments, the success or failure of feature-based image registration is largely determined by changes in lighting. This paper reports on a method to learn visual feature point descriptors that are more robust to changes in scene lighting than standard hand-designed features. We demonstrate that, by tracking feature points in time-lapse videos, one can easily generate training data that captures how the visual appearance of interest points changes with lighting over time. This training data is used to learn feature descriptors that map the image patches associated with feature points to a lower-dimensional feature space where Euclidean distance provides good discrimination between matching and non-matching image patches. Results showing that the learned descriptors increase the ability to register images under varying lighting conditions are presented for a challenging indoor-outdoor dataset spanning 27 mapping sessions over a period of 15 months, containing a wide variety of lighting changes.