Optimal Pose Estimation Method for a Multi-Segment, Programmable Bevel-Tip Steerable Needle

Sie sind hier: Homepage > Suche

Optimal Pose Estimation Method for a Multi-Segment, Programmable Bevel-Tip Steerable Needle

Freier Zugriff

Favaro, Alberto / Secoli, Riccardo / y Baena, Ferdinando Rodriguez / De Momi, Elena

Needle pose tracking is fundamental to achieve a precise and safe insertion in minimally-invasive percutaneous interventions. In this work, a method for estimating the full pose of steerable needles is presented, considering a four-segment Programmable Bevel-Tip Needle (PBN) as a case study. The method estimates also the torsion of the needle that can arise during the insertion because of the interaction forces exerted between the needle and the insertion medium. A novel 3D kinematic model of the PBN is developed and used to predict the full needle pose during the insertion through an Extended Kalman Filter. The filter uses the position measurements provided by electromagnetic sensors located at the tip of the PBN segments as measurement data. The feasibility of the proposed solution is verified through ingelatin experiments, demonstrating remarkable performance with small errors in position (RMSE<1 mm) and orientation (RMSE< 3°) estimation, as well as good accuracy compared to a bespoke geometric pose reconstruction method.

Zugriff

Download

Exportieren, teilen und zitieren

Dokumentinformationen

Titel :

Optimal Pose Estimation Method for a Multi-Segment, Programmable Bevel-Tip Steerable Needle

Beteiligte:

Favaro, Alberto (Autor:in) / Secoli, Riccardo (Autor:in) / y Baena, Ferdinando Rodriguez (Autor:in) / De Momi, Elena (Autor:in) / Favaro, Alberto / Secoli, Riccardo / y Baena, Ferdinando Rodriguez / De Momi, Elena

Erscheinungsdatum :

2020-01-01

DOI :

https://doi.org/10.1109/IROS45743.2020.9340847

Medientyp :

Aufsatz (Konferenz)

Format :

Elektronische Ressource

Sprache :

Englisch

Schlagwörter :

Three-dimensional displays , Pose estimation , Kinematics , Position measurement , Needles , Time measurement , Kalman filters