  Registration algorithms, such as Iterative Closest Point (ICP), have proven
effective in mobile robot localization algorithms over the last decades.
However, they are susceptible to failure when a robot sustains extreme
velocities and accelerations. For example, this kind of motion can happen after
a collision, causing a point cloud to be heavily skewed. While point cloud
de-skewing methods have been explored in the past to increase localization and
mapping accuracy, these methods still rely on highly accurate odometry systems
or ideal navigation conditions. In this paper, we present a method taking into
account the remaining motion uncertainties of the trajectory used to de-skew a
point cloud along with the environment geometry to increase the robustness of
current registration algorithms. We compare our method to three other solutions
in a test bench producing 3D maps with peak accelerations of 200 m/s^2 and 800
rad/s^2. In these extreme scenarios, we demonstrate that our method decreases
the error by 9.26 % in translation and by 21.84 % in rotation. The proposed
method is generic enough to be integrated to many variants of weighted ICP
without adaptation and supports localization robustness in harsher terrains.
