Optimal Pose Estimation Method for a Multi-Segment, Programmable Bevel-Tip Steerable Needle
Alberto Favaro, Riccardo Secoli, Ferdinando Rodriguez y Baena, Elena De Momi
Abstract
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.
BibTeX
@inproceedings{iros2020_optimalposeestim,
title = {Optimal Pose Estimation Method for a Multi-Segment, Programmable Bevel-Tip Steerable Needle},
author = {Alberto Favaro and Riccardo Secoli and Ferdinando Rodriguez y Baena and Elena De Momi},
booktitle = {IROS 2020},
year = {2020}
}