  With the democratization of 3D LiDAR sensors, precise LiDAR odometries and
SLAM are in high demand. New methods regularly appear, proposing solutions
ranging from small variations in classical algorithms to radically new
paradigms based on deep learning. Yet it is often difficult to compare these
methods, notably due to the few datasets on which the methods can be evaluated
and compared. Furthermore, their weaknesses are rarely examined, often letting
the user discover the hard way whether a method would be appropriate for a use
case. In this paper, we review and organize the main 3D LiDAR odometries into
distinct categories. We implemented several approaches (geometric based, deep
learning based, and hybrid methods) to conduct an in-depth analysis of their
strengths and weaknesses on multiple datasets, guiding the reader through the
different LiDAR odometries available. Implementation of the methods has been
made publicly available at
https://gitlab.kitware.com/keu-computervision/pylidar-slam.
