Adaptive 3D pose computation of suturing needle using constraints from static monocular image feedback
Fangxun Zhong, David Navarro-Alarcon, Zerui Wang, Yun-hui Liu, Tianxue Zhang, Hiu Man Yip, Hesheng Wang
Abstract
In this paper, we address the problem of the image-based 3D pose computation of a semi-circle suturing needle using monocular image feedback for laparoscopy. We propose a constrained two-degree-of-freedom (2-DOF) geometry-based modelling method to parametrise the needle's 6-DOF pose, including depth information. The modelling solely relies on the simultaneous observation of the needle's apparent tip and junction. No external markers are needed for extra constraints. An adaptive controller combining gradient descent and vector-flow method is introduced to iteratively guide the needle's initial guessing pose to its real pose by minimizing image-based position errors. Experiments have been conducted using both numerical simulations and simulated laparoscopic scenarios to evaluate the performance of the algorithm.
BibTeX
@inproceedings{iros2016_adaptive3dposeco,
title = {Adaptive 3D pose computation of suturing needle using constraints from static monocular image feedback},
author = {Fangxun Zhong and David Navarro-Alarcon and Zerui Wang and Yun-hui Liu and Tianxue Zhang and Hiu Man Yip and Hesheng Wang},
booktitle = {IROS 2016},
year = {2016}
}