Robust Surgical Tool Tracking with Pixel-based Probabilities for
  Projected Geometric Primitives
Christopher D'Ambrosia Florian Richter Zih-Yun Chiu Nikhil Shinde Fei Liu Henrik I. Christensen Michael C. Yip
Controlling robotic manipulators via visual feedback requires a known
coordinate frame transformation between the robot and the camera. Uncertainties
in mechanical systems as well as camera calibration create errors in this
coordinate frame transformation. These errors result in poor localization of
robotic manipulators and create a significant challenge for applications that
rely on precise interactions between manipulators and the environment. In this
work, we estimate the camera-to-base transform and joint angle measurement
errors for surgical robotic tools using an image based insertion-shaft
detection algorithm and probabilistic models. We apply our proposed approach in
both a structured environment as well as an unstructured environment and
measure to demonstrate the efficacy of our methods.