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

• URL: http://arxiv.org/abs/2601.14871v1
• Date: Wed, 21 Jan 2026 10:58:28 GMT
• Title: On-the-fly hand-eye calibration for the da Vinci surgical robot
• Abstract summary: We propose a calibration framework to produce accurate tool localization results through computing the hand-eye transformation matrix on-the-fly.<n>Results show a significant reduction in tool localization errors under the proposed calibration framework.
• Score: 35.18343759591773
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Real-time Rendering-based Surgical Instrument Tracking via Evolutionary Optimization [21.436806983625885]
  We propose a versatile tracking pipeline that jointly estimates surgical instrument pose and joint configurations.<n>The proposed framework further generalizes to joint angle-free and bi-manual tracking settings, making it suitable for both vision feedback control and online surgery video calibration.
  arXiv  Detail & Related papers  (2026-03-12T00:30:30Z)
• Efficient Surgical Robotic Instrument Pose Reconstruction in Real World Conditions Using Unified Feature Detection [21.460727996614704]
  MIS robots have long kinematic chains and partial visibility of their degrees of freedom in the camera.<n>We propose a novel framework that unifies the detection of geometric primitives through a shared encoding.<n>This architecture detects both keypoints and edges in a single inference and is trained on large-scale synthetic data with projective labeling.
  arXiv  Detail & Related papers  (2025-10-03T22:03:28Z)
• Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives [28.857732667640068]
  Controlling robotic manipulators via visual feedback requires a known coordinate frame transformation between the robot and the camera. Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation. We estimate the camera-to-base transform and joint angle measurement errors for surgical robotic tools using an image based insertion-shaft detection algorithm and probabilistic models.
  arXiv  Detail & Related papers  (2024-03-08T00:57:03Z)
• Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries [29.201385352740555]
  We propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. A cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation.
  arXiv  Detail & Related papers  (2023-09-02T14:52:58Z)
• Online estimation of the hand-eye transformation from surgical scenes [11.797350284719803]
  We present a neural network-based solution that estimates the transformation from a sequence of images and kinematic data. The proposed algorithm shows that the calibration procedure can be simplified by using deep learning techniques.
  arXiv  Detail & Related papers  (2023-06-04T04:55:02Z)
• Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [64.59698930334012]
  We present a multi-camera capture setup consisting of static and head-mounted cameras.<n>Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre.<n>Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
  arXiv  Detail & Related papers  (2023-05-05T13:42:19Z)
• EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration [49.90228618894857]
  We introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and delivers superior accuracy and robustness. We propose to use two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration. Our evaluation demonstrates superior performance in synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-02T03:49:54Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups [68.8204255655161]
  We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
  arXiv  Detail & Related papers  (2021-01-12T12:02:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.