  Surgical robots are controlled using a priori models based on robots'
geometric parameters, which are calibrated before the surgical procedure. One
of the challenges in using robots in real surgical settings is that parameters
change over time, consequently deteriorating control accuracy. In this context,
our group has been investigating online calibration strategies without added
sensors. In one step toward that goal, we have developed an algorithm to
estimate the pose of the instruments' shafts in endoscopic images. In this
study, we build upon that earlier work and propose a new framework to more
precisely estimate the pose of a rigid surgical instrument. Our strategy is
based on a novel pose estimation model called MBAPose and the use of synthetic
training data. Our experiments demonstrated an improvement of 21 % for
translation error and 26 % for orientation error on synthetic test data with
respect to our previous work. Results with real test data provide a baseline
for further research.
