  In this paper, we learn visual features that we use to first build a map and
then localize a robot driving autonomously across a full day of lighting
change, including in the dark. We train a neural network to predict sparse
keypoints with associated descriptors and scores that can be used together with
a classical pose estimator for localization. Our training pipeline includes a
differentiable pose estimator such that training can be supervised with ground
truth poses from data collected earlier, in our case from 2016 and 2017
gathered with multi-experience Visual Teach and Repeat (VT&R). We insert the
learned features into the existing VT&R pipeline to perform closed-loop path
following in unstructured outdoor environments. We show successful path
following across all lighting conditions despite the robot's map being
constructed using daylight conditions. Moreover, we explore generalizability of
the features by driving the robot across all lighting conditions in new areas
not present in the feature training dataset. In all, we validated our approach
with 35.5 km of autonomous path following experiments in challenging
conditions.
