TY - GEN
T1 - Online ultrasound sensor calibration using gradient descent on the Euclidean Group
AU - Ackerman, Martin Kendal
AU - Cheng, Alexis
AU - Boctor, Emad
AU - Chirikjian, Gregory
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/22
Y1 - 2014/9/22
N2 - Ultrasound imaging can be an advantageous imaging modality for image guided surgery. When using ultrasound imaging (or any imaging modality), calibration is important when more advanced forms of guidance, such as augmented reality systems, are used. There are many different methods of calibration, but the goal of each is to recover the rigid body transformation relating the pose of the probe to the ultrasound image frame. This paper presents a unified algorithm that can solve the ultrasound calibration problem for various calibration methodologies. The algorithm uses gradient descent optimization on the Euclidean Group. It can be used in real time, also serving as a way to update the calibration parameters on-line. We also show how filtering, based on the theory of invariants, can further improve the online results. Focusing on two specific calibration methodologies, the AX = XB problem and the BX-1p problem, we demonstrate the efficacy of the algorithm in both simulation and experimentation.
AB - Ultrasound imaging can be an advantageous imaging modality for image guided surgery. When using ultrasound imaging (or any imaging modality), calibration is important when more advanced forms of guidance, such as augmented reality systems, are used. There are many different methods of calibration, but the goal of each is to recover the rigid body transformation relating the pose of the probe to the ultrasound image frame. This paper presents a unified algorithm that can solve the ultrasound calibration problem for various calibration methodologies. The algorithm uses gradient descent optimization on the Euclidean Group. It can be used in real time, also serving as a way to update the calibration parameters on-line. We also show how filtering, based on the theory of invariants, can further improve the online results. Focusing on two specific calibration methodologies, the AX = XB problem and the BX-1p problem, we demonstrate the efficacy of the algorithm in both simulation and experimentation.
UR - http://www.scopus.com/inward/record.url?scp=84929179709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929179709&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6907577
DO - 10.1109/ICRA.2014.6907577
M3 - Conference contribution
AN - SCOPUS:84929179709
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4900
EP - 4905
BT - Proceedings - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Y2 - 31 May 2014 through 7 June 2014
ER -