  Covariance estimation for the Iterative Closest Point (ICP) point cloud
registration algorithm is essential for state estimation and sensor fusion
purposes. We argue that a major source of error for ICP is in the input data
itself, from the sensor noise to the scene geometry. Benefiting from recent
developments in deep learning for point clouds, we propose a data-driven
approach to learn an error model for ICP. We estimate covariances modeling
data-dependent heteroscedastic aleatoric uncertainty, and epistemic uncertainty
using a variational Bayesian approach. The system evaluation is performed on
LiDAR odometry on different datasets, highlighting good results in comparison
to the state of the art.
