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

arXiv: https://arxiv.org/abs/2102.06235 · HTML: https://mlsanity.com/paper/2102.06235/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2021.

## Built on by (most cited first, 17 of 17)

- [Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer](https://mlsanity.com/paper/2302.14332/) (2023)
- [Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking](https://mlsanity.com/paper/2210.16674/) (2022)
- [Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning](https://mlsanity.com/paper/2301.00452/) (2023)
- [CtRNet-X: Camera-to-Robot Pose Estimation in Real-world Conditions Using a Single Camera](https://mlsanity.com/paper/2409.10441/) (2024)
- [Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Rendering](https://mlsanity.com/paper/2302.14039/) (2023)
- [Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty Estimation for Autonomous Minimally Invasive Robotic Surgery](https://mlsanity.com/paper/2109.12722/) (2021)
- [BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields](https://mlsanity.com/paper/2309.15329/) (2023)
- [Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives](https://mlsanity.com/paper/2403.04971/) (2024)
- [RobotSmith: Generative Robotic Tool Design for Acquisition of Complex Manipulation Skills](https://mlsanity.com/paper/2506.14763/) (2025)
- [Efficient Surgical Robotic Instrument Pose Reconstruction in Real World Conditions Using Unified Feature Detection](https://mlsanity.com/paper/2510.03532/) (2025)
- [Real-Time Constrained 6D Object-Pose Tracking of An In-Hand Suture Needle for Minimally Invasive Robotic Surgery](https://mlsanity.com/paper/2210.11973/) (2022)
- [SuPerPM: A Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data](https://mlsanity.com/paper/2309.13863/) (2023)
- [Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries](https://mlsanity.com/paper/2309.00957/) (2023)
- [Real-time Rendering-based Surgical Instrument Tracking via Evolutionary Optimization](https://mlsanity.com/paper/2603.11404/) (2026)
- [SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning](https://mlsanity.com/paper/2409.15651/) (2024)
- [arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgery](https://mlsanity.com/paper/2603.26814/) (2026)
- [Robust Surgical Robotic Instrument Tracking via Sequential Multi-Cue Fusion and Sim-to-Real Self-Training](https://mlsanity.com/paper/2610.05491/) (2026)

## 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.