# Real-Time Constrained 6D Object-Pose Tracking of An In-Hand Suture Needle for Minimally Invasive Robotic Surgery

arXiv: https://arxiv.org/abs/2210.11973 · HTML: https://mlsanity.com/paper/2210.11973/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics](https://mlsanity.com/paper/1909.05405/) (2019)
- [SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction](https://mlsanity.com/paper/2003.03472/) (2020)
- [Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning](https://mlsanity.com/paper/2011.04813/) (2020)
- [Robot Autonomy for Surgery](https://mlsanity.com/paper/1707.03080/) (2017)
- [Robotic Tool Tracking under Partially Visible Kinematic Chain: A Unified Approach](https://mlsanity.com/paper/2102.06235/) (2021)
- [Autonomous Robotic Suction to Clear the Surgical Field for Hemostasis using Image-based Blood Flow Detection](https://mlsanity.com/paper/2010.08441/) (2020)
- [Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles](https://mlsanity.com/paper/2112.04071/) (2021)
- [Automated pick-up of suturing needles for robotic surgical assistance](https://mlsanity.com/paper/1804.03141/) (2018)
- [Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty Estimation for Autonomous Minimally Invasive Robotic Surgery](https://mlsanity.com/paper/2109.12722/) (2021)

## Built on by (most cited first, 2 of 2)

- [Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives](https://mlsanity.com/paper/2403.04971/) (2024)
- [SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning](https://mlsanity.com/paper/2409.15651/) (2024)

## Abstract (arXiv)

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.