  Surgical robots are usually controlled using a priori models based on the
robots' geometric parameters, which are calibrated before the surgical
procedure. One of the challenges in using robots in real surgical settings is
that those parameters can 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.
