Vision-based calibration of dual RCM-based robot arms in human-robot collaborative minimally invasive surgery

Zerui Wang, Ziwei Liu, Qianli Ma, Alexis Cheng, Yun Hui Liu, Sungmin Kim, Anton Deguet, Austin Reiter, Peter Kazanzides, Russell H. Taylor

Research output: Contribution to journalArticlepeer-review


This letter reports the development of a vision-based calibration method for dual remote center-of-motion (RCM) based robot arms in a human-robot collaborative minimally invasive surgery (MIS) scenario. The method does not require any external tracking sensors and directly uses images captured by the endoscopic camera and the robot encoder readings as calibration data, which leads to a minimal and practical system in the operating room. By taking advantage of the motion constraints imposed by the RCM-based kinematics of the robotic surgical tools and cameras, we can find unique relationships between the endoscope and the surgical tool using camera perspective projection geometry without the geometric information of the tool. A customized vision-based centerline detection algorithm is also proposed, which provides robust estimation of centerline positions for a variety of settings. We validate the method through simulations and an experimental study in a simulated MIS scenario, in which the first generation da Vinci surgical system controlled by the open source da Vinci research kit electronics and cisst/SAW software environment is used.

Original languageEnglish (US)
Pages (from-to)672-679
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2018


  • Calibration and identification
  • Computer vision for medical robotics
  • Laparoscopy
  • Surgical robotics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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