Feature Classification for Tracking Articulated Surgical Tools

Abstract

Tool tracking is an accepted capability for computer-aided surgical intervention which has numerous applications, both in robotic and manual minimally-invasive procedures. In this paper, we describe a tracking system which learns visual feature descriptors as class-specific landmarks on an articulated tool. The features are localized in 3D using stereo vision and are fused with the robot kinematics to track all of the joints of the dexterous manipulator. Experiments are performed using previously-collected porcine data from a surgical robot.