  Compared to LiDAR-based localization methods, which provide high accuracy but
rely on expensive sensors, visual localization approaches only require a camera
and thus are more cost-effective while their accuracy and reliability typically
is inferior to LiDAR-based methods. In this work, we propose a vision-based
localization approach that learns from LiDAR-based localization methods by
using their output as training data, thus combining a cheap, passive sensor
with an accuracy that is on-par with LiDAR-based localization. The approach
consists of two deep networks trained on visual odometry and topological
localization, respectively, and a successive optimization to combine the
predictions of these two networks. We evaluate the approach on a new
challenging pedestrian-based dataset captured over the course of six months in
varying weather conditions with a high degree of noise. The experiments
demonstrate that the localization errors are up to 10 times smaller than with
traditional vision-based localization methods.
