Deep Bayesian ICP Covariance Estimation - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Deep Bayesian ICP Covariance Estimation

Andrea de Maio
Simon Lacroix

Résumé

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.
Fichier principal
Vignette du fichier
2202.11607.pdf (1.47 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03766231 , version 1 (31-08-2022)

Identifiants

Citer

Andrea de Maio, Simon Lacroix. Deep Bayesian ICP Covariance Estimation. IEEE International Conference on Robotics and Automation (ICRA 2022), May 2022, Philadelphia, United States. pp.6519-6525, ⟨10.1109/ICRA46639.2022.9811899⟩. ⟨hal-03766231⟩
13 Consultations
31 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More