  In the last decades, Light Detection And Ranging (LiDAR) technology has been
extensively explored as a robust alternative for self-localization and mapping.
These approaches typically state ego-motion estimation as a non-linear
optimization problem dependent on the correspondences established between the
current point cloud and a map, whatever its scope, local or global. This paper
proposes LiODOM, a novel LiDAR-only ODOmetry and Mapping approach for pose
estimation and map-building, based on minimizing a loss function derived from a
set of weighted point-to-line correspondences with a local map abstracted from
the set of available point clouds. Furthermore, this work places a particular
emphasis on map representation given its relevance for quick data association.
To efficiently represent the environment, we propose a data structure that
combined with a hashing scheme allows for fast access to any section of the
map. LiODOM is validated by means of a set of experiments on public datasets,
for which it compares favourably against other solutions. Its performance
on-board an aerial platform is also reported.
