Online ultrasound sensor calibration using gradient descent on the Euclidean Group

Martin Kendal Ackerman, Alexis Cheng, Emad Boctor, Gregory Chirikjian

Research output: Contribution to journalConference article


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.

Original languageEnglish (US)
Article number6907577
Pages (from-to)4900-4905
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Sep 22 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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