  In a context of 3D mapping, it is very important to get accurate measurements
from sensors. In particular, Light Detection And Ranging (LIDAR) measurements
are typically treated as a zero-mean Gaussian distribution. We show that this
assumption leads to predictable localisation drifts, especially when a bias
related to measuring obstacles with high incidence angles is not taken into
consideration. Moreover, we present a way to physically understand and model
this bias, which generalises to multiple sensors. Using an experimental setup,
we measured the bias of the Sick LMS151, Velodyne HDL-32E, and Robosense
RS-LiDAR-16 as a function of depth and incidence angle, and showed that the
bias can go up to 20 cm for high incidence angles. We then used our
modelisations to remove the bias from the measurements, leading to more
accurate maps and a reduced localisation drift.
