  Localization of a robotic system within a previously mapped environment is
important for reducing estimation drift and for reusing previously built maps.
Existing techniques for geometry-based localization have focused on the
description of local surface geometry, usually using pointclouds as the
underlying representation. We propose a system for geometry-based localization
that extracts features directly from an implicit surface representation: the
Signed Distance Function (SDF). The SDF varies continuously through space,
which allows the proposed system to extract and utilize features describing
both surfaces and free-space. Through evaluations on public datasets, we
demonstrate the flexibility of this approach, and show an increase in
localization performance over state-of-the-art handcrafted surfaces-only
descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and
~18% on a LiDAR-based dataset. Finally, we demonstrate our system for
localizing a LiDAR-equipped MAV within a previously built map of a search and
rescue training ground.
