ArXiv Preprint
Autonomous suturing has been a long-sought-after goal for surgical robotics.
Outside of staged environments, accurate localization of suture needles is a
critical foundation for automating various suture needle manipulation tasks in
the real world. When localizing a needle held by a gripper, previous work
usually tracks them separately without considering their relationship. Because
of the significant errors that can arise in the stereo-triangulation of objects
and instruments, their reconstructions may often not be consistent. This can
lead to unrealistic tool-needle grasp reconstructions that are infeasible.
Instead, an obvious strategy to improve localization would be to leverage
constraints that arise from contact, thereby constraining reconstructions of
objects and instruments into a jointly feasible space. In this work, we
consider feasible grasping constraints when tracking the 6D pose of an in-hand
suture needle. We propose a reparameterization trick to define a new state
space for describing a needle pose, where grasp constraints can be easily
defined and satisfied. Our proposed state space and feasible grasping
constraints are then incorporated into Bayesian filters for real-time needle
localization. In the experiments, we show that our constrained methods
outperform previous unconstrained/constrained tracking approaches and
demonstrate the importance of incorporating feasible grasping constraints into
automating suture needle manipulation tasks.
Zih-Yun Chiu, Florian Richter, Michael C. Yip
2022-10-21
