CELLO-3D: Estimating the Covariance of ICP in the Real World
David Landry, Francois Pomerleau, Philippe Giguère
Abstract
The fusion of Iterative Closest Point (ICP) registrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. In this paper, we study the estimation of this uncertainty in the form of a covariance. First, we scrutinize the limitations of existing closed-form covariance estimation algorithms over 3D datasets. Then, we set out to estimate the covariance of ICP registrations through a data-driven approach, with over 5100000 registrations on 1020 pairs from real 3D point clouds. We assess our solution upon a wide spectrum of environments, ranging from structured to unstructured and indoor to outdoor. The capacity of our algorithm to predict covariances is accurately assessed, as well as the usefulness of these estimations for uncertainty estimation over trajectories. The proposed method estimates covariances better than existing closed-form solutions, and makes predictions that are consistent with observed trajectories.
BibTeX
@inproceedings{icra2019_cello3destimatin,
title = {CELLO-3D: Estimating the Covariance of ICP in the Real World},
author = {David Landry and Francois Pomerleau and Philippe Giguère},
booktitle = {ICRA 2019},
year = {2019}
}