  Automation in surgical robotics has the potential to improve patient safety
and surgical efficiency, but it is difficult to achieve due to the need for
robust perception algorithms. In particular, 6D pose estimation of surgical
instruments is critical to enable the automatic execution of surgical maneuvers
based on visual feedback. In recent years, supervised deep learning algorithms
have shown increasingly better performance at 6D pose estimation tasks; yet,
their success depends on the availability of large amounts of annotated data.
In household and industrial settings, synthetic data, generated with 3D
computer graphics software, has been shown as an alternative to minimize
annotation costs of 6D pose datasets. However, this strategy does not translate
well to surgical domains as commercial graphics software have limited tools to
generate images depicting realistic instrument-tissue interactions. To address
these limitations, we propose an improved simulation environment for surgical
robotics that enables the automatic generation of large and diverse datasets
for 6D pose estimation of surgical instruments. Among the improvements, we
developed an automated data generation pipeline and an improved surgical scene.
To show the applicability of our system, we generated a dataset of 7.5k images
with pose annotations of a surgical needle that was used to evaluate a
state-of-the-art pose estimation network. The trained model obtained a mean
translational error of 2.59mm on a challenging dataset that presented varying
levels of occlusion. These results highlight our pipeline's success in training
and evaluating novel vision algorithms for surgical robotics applications.
