Literature DB >> 32808149

Vision-based hand-eye calibration for robot-assisted minimally invasive surgery.

Yanwen Sun1, Bo Pan2, Yongchen Guo1, Yili Fu1, Guojun Niu3.   

Abstract

PURPOSE: The knowledge of laparoscope vision can greatly improve the surgical operation room (OR) efficiency. For the vision-based computer-assisted surgery, the hand-eye calibration establishes the coordinate relationship between laparoscope and robot slave arm. While significant advances have been made for hand-eye calibration in recent years, efficient algorithm for minimally invasive surgical robot is still a major challenge. Removing the external calibration object in abdominal environment to estimate the hand-eye transformation is still a critical problem.
METHODS: We propose a novel hand-eye calibration algorithm to tackle the problem which relies purely on surgical instrument already in the operating scenario for robot-assisted minimally invasive surgery (RMIS). Our model is formed by the geometry information of the surgical instrument and the remote center-of-motion (RCM) constraint. We also enhance the algorithm with stereo laparoscope model.
RESULTS: Promising validation of synthetic simulation and experimental surgical robot system have been conducted to evaluate the proposed method. We report results that the proposed method can exhibit the hand-eye calibration without calibration object.
CONCLUSION: Vision-based hand-eye calibration is developed. We demonstrate the feasibility to perform hand-eye calibration by taking advantage of the components of surgical robot system, leading to the efficiency of surgical OR.

Keywords:  Hand–eye calibration; Minimally invasive surgery; Stereo laparoscope; Surgical robot

Mesh:

Year:  2020        PMID: 32808149     DOI: 10.1007/s11548-020-02245-5

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  1 in total

1.  Physician-Friendly Tool Center Point Calibration Method for Robot-Assisted Puncture Surgery.

Authors:  Leifeng Zhang; Changle Li; Yilun Fan; Xuehe Zhang; Jie Zhao
Journal:  Sensors (Basel)       Date:  2021-01-07       Impact factor: 3.576

  1 in total

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