mlsanityData to 7 Oct 2026

Robotic Tool Tracking under Partially Visible Kinematic Chain: A Unified Approach

arXiv 2102.06235 ↗First posted 11 Feb 2021cs.ROcited by 16

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Abstract (arXiv)

Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are non-identifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call Lumped Error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the Lumped Error and show the efficiency of this parameter reduction.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 6; 2024: 3; 2025: 2; 2026: 2.

16 papers.

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  1. 2023Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfercited by 23
  2. 2022Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Trackingcited by 19
  3. 2023Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learningcited by 11
  4. 2024CtRNet-X: Camera-to-Robot Pose Estimation in Real-world Conditions Using a Single Cameracited by 10
  5. 2023Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Renderingcited by 9
  6. 2021Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty Estimation for Autonomous Minimally Invasive Robotic Surgerycited by 5
  7. 2023BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fieldscited by 5
  8. 2025RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skillscited by 4
  9. 2024Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitivescited by 3
  10. 2022Real-Time Constrained 6D Object-Pose Tracking of An In-Hand Suture Needle for Minimally Invasive Robotic Surgerycited by 2
  11. 2023SuPerPM: A Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Datacited by 2
  12. 2025Efficient Surgical Robotic Instrument Pose Reconstruction in Real World Conditions Using Unified Feature Detectioncited by 2
4 more
  1. 2023Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeriescited by 1
  2. 2024SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learningcited by 0
  3. 2026Real-time Rendering-based Surgical Instrument Tracking via Evolutionary Optimizationcited by 0
  4. 2026arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgerycited by 0