Monocular feature-based periodic motion estimation for surgical guidance

In this paper, we present a novel approach for mapping periodically moving visual features with a monocular camera. Our target application is the estimation of moving surfaces during minimally invasive surgery for the purpose of aiding in the guidance of surgical tools. Our approach uses a bank of Kalman filters to estimate FFT parameters that encode the periodic motion of visually detected features. To ensure convergent estimation for this highly nonlinear problem, we have developed an iterative update procedure that treats the Kalman filter measurement update step as an optimization problem. Unlike existing solutions that rely on stereo vision, our approach estimates periodic motion with a single moving camera. With an experiment involving a beating heart phantom, we have shown that our approach is able to successfully estimate the periodic motion of visual features.