  Person detection is a crucial task for mobile robots navigating in
human-populated environments. LiDAR sensors are promising for this task, thanks
to their accurate depth measurements and large field of view. Two types of
LiDAR sensors exist: the 2D LiDAR sensors, which scan a single plane, and the
3D LiDAR sensors, which scan multiple planes, thus forming a volume. How do
they compare for the task of person detection? To answer this, we conduct a
series of experiments, using the public, large-scale JackRabbot dataset and the
state-of-the-art 2D and 3D LiDAR-based person detectors (DR-SPAAM and
CenterPoint respectively). Our experiments include multiple aspects, ranging
from the basic performance and speed comparison, to more detailed analysis on
localization accuracy and robustness against distance and scene clutter. The
insights from these experiments highlight the strengths and weaknesses of 2D
and 3D LiDAR sensors as sources for person detection, and are especially
valuable for designing mobile robots that will operate in close proximity to
surrounding humans (e.g. service or social robot).
