Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives

Proc. IEEE International Conference on Robotics and Automation (ICRA), 2024

Christopher D’Ambrosia, Florian Richter, Zih-Yun Chiu, Nikhil Shinde, Fei Liu, Henrik I Christensen, Michael C Yip

Abstract: 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.

D’Ambrosia et al. (2024) Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives, Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 15455-15462.

Pub Link: http://arxiv.org/pdf/2403.04971
arXiv: