Vision-Based Surgical Tool Pose Estimation for the da Vinci^® Robotic Surgical System

Area of Science:

• Robotics
• Computer Vision
• Surgical Technology

Background:

• Accurate surgical tool tracking is crucial for enhancing precision and safety in robot-assisted minimally invasive surgery.
• Existing methods for tracking surgical instruments in robotic systems face challenges with real-time performance and accuracy.

Purpose of the Study:

• To develop and evaluate a novel stereo vision-based approach for precise surgical tool tracking within the da Vinci Surgical Robotic System.
• To leverage robot kinematics, computer vision, and Bayesian state estimation for robust instrument localization.

Main Methods:

• A silhouette rendering algorithm generates virtual tool images from defined geometry under da Vinci robot endoscopes.
• The distance between rendered and real tools is measured from endoscopic stereo image streams.
• A Particle Filter algorithm integrates virtual rendering for surgical tool tracking.

Main Results:

• The proposed method demonstrates effective surgical tool tracking capabilities.
• Performance was validated on both a physical da Vinci surgical robotic system and a ROS/Gazebo simulation environment.
• The approach enables accurate measurement of tool position from stereo endoscopic views.

Conclusions:

• The developed stereo vision approach offers a viable solution for accurate surgical tool tracking in the da Vinci Surgical Robotic System.
• This method enhances the potential for improved surgical guidance and automation in robotic surgery.
• The combination of virtual rendering and Particle Filtering provides a robust framework for instrument localization.