On-the-fly hand-eye calibration for the da Vinci surgical robot

TL;DR

This paper introduces an on-the-fly calibration framework for the da Vinci surgical robot that improves tool localization accuracy without pre-training, adaptable to various scenarios, and validated on multiple datasets.

Contribution

The study presents a novel real-time calibration method combining feature association and hand-eye calibration algorithms for cable-driven surgical robots.

Findings

Significant reduction in tool localization errors.

Comparable accuracy to state-of-the-art methods.

Enhanced time efficiency in calibration process.

Abstract

In Robot-Assisted Minimally Invasive Surgery (RMIS), accurate tool localization is crucial to ensure patient safety and successful task execution. However, this remains challenging for cable-driven robots, such as the da Vinci robot, because erroneous encoder readings lead to pose estimation errors. In this study, we propose a calibration framework to produce accurate tool localization results through computing the hand-eye transformation matrix on-the-fly. The framework consists of two interrelated algorithms: the feature association block and the hand-eye calibration block, which provide robust correspondences for key points detected on monocular images without pre-training, and offer the versatility to accommodate various surgical scenarios by adopting an array of filter approaches, respectively. To validate its efficacy, we test the framework extensively on publicly available video datasets that feature multiple surgical instruments conducting tasks in both in vitro and ex vivo scenarios, under varying illumination conditions and with different levels of key point measurement accuracy. The results show a significant reduction in tool localization errors under the proposed calibration framework, with accuracies comparable to other state-of-the-art methods while being more time-efficient.