TY - GEN
T1 - Surgical tool pose estimation from monocular endoscopic videos
AU - Kumar, Suren
AU - Sovizi, Javad
AU - Narayanan, Madusudanan Sathia
AU - Krovi, Venkat
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/29
Y1 - 2015/6/29
N2 - Surgical tool pose estimation has been proven to be useful for high- and low- level feedback tasks including safety-enhancement, semantic feedback and surgical skill assessment. Tool pose estimation using monocular camera input is a well-studied research problem as the monocular camera is one of the ubiquitous sensor across the spectrum of robotic devices. Current state-of-the art methods for visual tool pose estimation are computationally expensive and require elaborate geometric and appearance models of surgical tools. We propose a visual tool pose estimation method that maps the visual bounding box to the 3D tool pose without any explicit knowledge of tool geometry using Gaussian process regression. The proposed approach can be generalized to any surgical tool and provides tool pose estimates with a variance estimate in real-time. We demonstrate rigorous evaluation of the method under various conditions that might effect the estimation process. In order to evaluate the algorithm, we have instrumented a standard box trainer kit with two laparoscopic tools to get simultaneous ground truth pose and a video feed.
AB - Surgical tool pose estimation has been proven to be useful for high- and low- level feedback tasks including safety-enhancement, semantic feedback and surgical skill assessment. Tool pose estimation using monocular camera input is a well-studied research problem as the monocular camera is one of the ubiquitous sensor across the spectrum of robotic devices. Current state-of-the art methods for visual tool pose estimation are computationally expensive and require elaborate geometric and appearance models of surgical tools. We propose a visual tool pose estimation method that maps the visual bounding box to the 3D tool pose without any explicit knowledge of tool geometry using Gaussian process regression. The proposed approach can be generalized to any surgical tool and provides tool pose estimates with a variance estimate in real-time. We demonstrate rigorous evaluation of the method under various conditions that might effect the estimation process. In order to evaluate the algorithm, we have instrumented a standard box trainer kit with two laparoscopic tools to get simultaneous ground truth pose and a video feed.
UR - http://www.scopus.com/inward/record.url?scp=84938220060&partnerID=8YFLogxK
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U2 - 10.1109/ICRA.2015.7139240
DO - 10.1109/ICRA.2015.7139240
M3 - Conference contribution
AN - SCOPUS:84938220060
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 598
EP - 603
BT - 2015 IEEE International Conference on Robotics and Automation, ICRA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE International Conference on Robotics and Automation, ICRA 2015
Y2 - 26 May 2015 through 30 May 2015
ER -