  Lifelong indoor localization in a given map is the basis for navigation of
autonomous mobile robots. In this letter, we address the problem of robust
localization in cluttered indoor environments like office spaces and corridors
using 3D LiDAR point clouds in a given Area Graph, which is a hierarchical,
topometric semantic map representation that uses polygons to demark areas such
as rooms, corridors or buildings. This representation is very compact, can
represent different floors of buildings through its hierarchy and provides
semantic information that helps with localization, like poses of doors and
glass. In contrast to this, commonly used map representations, such as
occupancy grid maps or point clouds, lack these features and require frequent
updates in response to environmental changes (e.g. moved furniture), unlike our
approach, which matches against lifelong architectural features such as walls
and doors. For that we apply filtering to remove clutter from the 3D input
point cloud and then employ further scoring and weight functions for
localization. Given a broad initial guess from WiFi localization, our
experiments show that our global localization and the weighted point to line
ICP pose tracking perform very well, even when compared to localization and
SLAM algorithms that use the current, feature-rich cluttered map for
localization.
