  Visual localization is the task of estimating camera pose in a known scene,
which is an essential problem in robotics and computer vision. However,
long-term visual localization is still a challenge due to the environmental
appearance changes caused by lighting and seasons. While techniques exist to
address appearance changes using neural networks, these methods typically
require ground-truth pose information to generate accurate image
correspondences or act as a supervisory signal during training. In this paper,
we present a novel self-supervised feature learning framework for metric visual
localization. We use a sequence-based image matching algorithm across different
sequences of images (i.e., experiences) to generate image correspondences
without ground-truth labels. We can then sample image pairs to train a deep
neural network that learns sparse features with associated descriptors and
scores without ground-truth pose supervision. The learned features can be used
together with a classical pose estimator for visual stereo localization. We
validate the learned features by integrating with an existing Visual Teach &
Repeat pipeline to perform closed-loop localization experiments under different
lighting conditions for a total of 22.4 km.
