On-manifold probabilistic Iterative Closest Point: Application to underwater karst exploration - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue The International Journal of Robotics Research Année : 2022

On-manifold probabilistic Iterative Closest Point: Application to underwater karst exploration

Résumé

This paper proposes MpIC, an on-manifold derivation of the probabilistic Iterative Correspondence (pIC) algorithm, which is a stochastic version of the original Iterative Closest Point. It is developed in the context of autonomous underwater karst exploration based on acoustic sonars. First, a derivation of pIC based on the Lie group structure of S E ( 3 ) is developed. The closed-form expression of the covariance modeling the estimated rigid transformation is also provided. In a second part, its application to 3D scan matching between acoustic sonar measurements is proposed. It is a prolongation of previous work on elevation angle estimation from wide-beam acoustic sonar. While the pIC approach proposed is intended to be a key component in a Simultaneous Localization and Mapping framework, this paper focuses on assessing its viability on a unitary basis. As ground truth data in karst aquifer are difficult to obtain, quantitative experiments are carried out on a simulated karst environment and show improvement compared to previous state-of-the-art approach. The algorithm is also evaluated on a real underwater cave dataset demonstrating its practical applicability.
Fichier principal
Vignette du fichier
manuscript_preprint.pdf (14.05 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03182013 , version 1 (26-03-2021)
hal-03182013 , version 2 (07-04-2021)
hal-03182013 , version 3 (09-06-2021)

Identifiants

Citer

Yohan Breux, André Mas, Lionel Lapierre. On-manifold probabilistic Iterative Closest Point: Application to underwater karst exploration. The International Journal of Robotics Research, 2022, 41 (9-10), pp.875-902. ⟨10.1177/02783649221101418⟩. ⟨hal-03182013v3⟩
145 Consultations
68 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More