mlsanityData to 14 Oct 2026

Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives

arXiv 2403.04971 ↗First posted 8 Mar 2024cs.RO cs.CVcited by 4

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Abstract (arXiv)

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. These errors result in poor localization of robotic manipulators and create a significant challenge for applications that rely on precise interactions between manipulators and the environment. In this work, 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. We apply our proposed approach in both a structured environment as well as an unstructured environment and measure to demonstrate the efficacy of our methods.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 8 Mar 2024.

Builds on

21 of 51 references.

References · 21

  1. 2015Holistically-Nested Edge Detectioncited by 592
  2. 2019Camera-to-Robot Pose Estimation from a Single Imagecited by 48
  3. 2019SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Roboticscited by 47
  4. 2021SOLD2: Self-supervised Occlusion-aware Line Description and Detectioncited by 47
  5. 2020SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extractioncited by 28
  6. 2023Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfercited by 24
  7. 2020Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planningcited by 21
  8. 2022Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Trackingcited by 19
  9. 2017Robot Autonomy for Surgerycited by 17
  10. 2021Robotic Tool Tracking under Partially Visible Kinematic Chain: A Unified Approachcited by 17
  11. 2016Probabilistic Articulated Real-Time Tracking for Robot Manipulationcited by 13
  12. 2020Autonomous Robotic Suction to Clear the Surgical Field for Hemostasis using Image-based Blood Flow Detectioncited by 12
9 more
  1. 2017Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedurecited by 11
  2. 2020Real-to-Sim Registration of Deformable Soft Tissue with Position-Based Dynamics for Surgical Robot Autonomycited by 11
  3. 2021Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needlescited by 10
  4. 2020Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robotcited by 8
  5. 2021Model-Predictive Control of Blood Suction for Surgical Hemostasis using Differentiable Fluid Simulationscited by 6
  6. 2021Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty Estimation for Autonomous Minimally Invasive Robotic Surgerycited by 5
  7. 2021From Bench to Bedside: The First Live Robotic Surgery on the dVRK to Enable Remote Telesurgery with Motion Scalingcited by 4
  8. 2019Real-time Data Driven Precision Estimator for RAVEN-II Surgical Robot End Effector Positioncited by 3
  9. 2022Real-Time Constrained 6D Object-Pose Tracking of An In-Hand Suture Needle for Minimally Invasive Robotic Surgerycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Efficient Surgical Robotic Instrument Pose Reconstruction in Real World Conditions Using Unified Feature Detectioncited by 3
  2. 2026Real-time Rendering-based Surgical Instrument Tracking via Evolutionary Optimizationcited by 1
  3. 2026arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgerycited by 0
  4. 2026Robust Surgical Robotic Instrument Tracking via Sequential Multi-Cue Fusion and Sim-to-Real Self-Trainingcited by 0