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

Area of Science:

• Robotics
• Medical Imaging
• Surgical Technology

Background:

• Hand-eye calibration is crucial for vision-based computer-assisted surgery, establishing coordinate relationships between laparoscopes and robot arms.
• Efficient algorithms for hand-eye calibration in robot-assisted minimally invasive surgery (RMIS) remain a challenge, particularly without external calibration objects.
• Current methods often require dedicated calibration objects, complicating the process in abdominal environments.

Purpose of the Study:

• To develop a novel, efficient hand-eye calibration algorithm for RMIS.
• To eliminate the need for external calibration objects in the surgical environment.
• To enhance the efficiency of surgical operation rooms through improved calibration techniques.

Main Methods:

• A new algorithm utilizing the geometry of surgical instruments and the remote center-of-motion (RCM) constraint within the operating scenario.
• Integration of a stereo laparoscope model to enhance the calibration algorithm.
• Validation through synthetic simulations and experimental surgical robot systems.

Main Results:

• Successful demonstration of hand-eye calibration without the use of external calibration objects.
• Validation of the proposed method in both simulated and experimental RMIS environments.
• The algorithm effectively leverages existing surgical robot components for calibration.

Conclusions:

• A novel vision-based hand-eye calibration method has been developed for RMIS.
• The feasibility of performing hand-eye calibration using intrinsic components of the surgical robot system is demonstrated.
• This approach significantly contributes to improving the efficiency of surgical operation rooms.