Details

Autor(en) / Beteiligte

• Fangxun Zhong
• Navarro-Alarcon, David
• Zerui Wang
• Yun-hui Liu
• Tianxue Zhang
• Hiu Man Yip
• Hesheng Wang

Titel

Adaptive 3D pose computation of suturing needle using constraints from static monocular image feedback

Ist Teil von

• 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, p.5521-5526

Ort / Verlag

IEEE

Erscheinungsjahr

2016

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.