  Localization in already mapped environments is a critical component in many
robotics and automotive applications, where previously acquired information can
be exploited along with sensor fusion to provide robust and accurate
localization estimates. In this work, we offer a new perspective on map-based
localization by reusing prior topological and metric information. Thus, we
reformulate this long-studied problem to go beyond the mere use of metric maps.
Our framework seamlessly integrates LiDAR, iner\-tial and GNSS measurements,
and scan-to-map registrations in a sliding window graph fashion, which allows
to accommodate the uncertainty of each observation. The modularity of our
framework allows it to work with different sensor configurations
(\textit{e.g.,} LiDAR resolutions, GNSS denial) and environmental conditions
(\textit{e.g.,} map-less regions, large environments). We have conducted
different validation experiments, including deployment in a real-world
automotive application, demonstrating the accuracy, efficiency, and versatility
of our system in online localization.
