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Kalib: Easy Hand-Eye Calibration with Reference Point Tracking

Tutian Tang, Minghao Liu, Wenqiang Xu, Cewu Lu

TL;DR

Kalib tackles the burden of hand-eye calibration in unstructured settings by proposing a markerless, training-free pipeline that tracks a fixed reference point on the robot via visual foundation models and uses forward kinematics plus a PnP solver to estimate the camera–robot transform. The method supports both eye-in-hand and eye-on-base configurations and requires only the robot’s kinematic chain and a single reference point, eliminating the need for fiducial boards or precise mesh models. Across simulation and real-world benchmarks, Kalib achieves competitive accuracy with substantially reduced manual setup and demonstrates robustness to noisy backgrounds and occlusions. The work highlights practical potential for continuous operation in unstructured environments and lays groundwork for further improvements in tracking robustness and calibration reliability.

Abstract

Hand-eye calibration aims to estimate the transformation between a camera and a robot. Traditional methods rely on fiducial markers, which require considerable manual effort and precise setup. Recent advances in deep learning have introduced markerless techniques but come with more prerequisites, such as retraining networks for each robot, and accessing accurate mesh models for data generation. In this paper, we propose Kalib, an automatic and easy-to-setup hand-eye calibration method that leverages the generalizability of visual foundation models to overcome these challenges. It features only two basic prerequisites, the robot's kinematic chain and a predefined reference point on the robot. During calibration, the reference point is tracked in the camera space. Its corresponding 3D coordinates in the robot coordinate can be inferred by forward kinematics. Then, a PnP solver directly estimates the transformation between the camera and the robot without training new networks or accessing mesh models. Evaluations in simulated and real-world benchmarks show that Kalib achieves good accuracy with a lower manual workload compared with recent baseline methods. We also demonstrate its application in multiple real-world settings with various robot arms and grippers. Kalib's user-friendly design and minimal setup requirements make it a possible solution for continuous operation in unstructured environments.

Kalib: Easy Hand-Eye Calibration with Reference Point Tracking

TL;DR

Kalib tackles the burden of hand-eye calibration in unstructured settings by proposing a markerless, training-free pipeline that tracks a fixed reference point on the robot via visual foundation models and uses forward kinematics plus a PnP solver to estimate the camera–robot transform. The method supports both eye-in-hand and eye-on-base configurations and requires only the robot’s kinematic chain and a single reference point, eliminating the need for fiducial boards or precise mesh models. Across simulation and real-world benchmarks, Kalib achieves competitive accuracy with substantially reduced manual setup and demonstrates robustness to noisy backgrounds and occlusions. The work highlights practical potential for continuous operation in unstructured environments and lays groundwork for further improvements in tracking robustness and calibration reliability.

Abstract

Hand-eye calibration aims to estimate the transformation between a camera and a robot. Traditional methods rely on fiducial markers, which require considerable manual effort and precise setup. Recent advances in deep learning have introduced markerless techniques but come with more prerequisites, such as retraining networks for each robot, and accessing accurate mesh models for data generation. In this paper, we propose Kalib, an automatic and easy-to-setup hand-eye calibration method that leverages the generalizability of visual foundation models to overcome these challenges. It features only two basic prerequisites, the robot's kinematic chain and a predefined reference point on the robot. During calibration, the reference point is tracked in the camera space. Its corresponding 3D coordinates in the robot coordinate can be inferred by forward kinematics. Then, a PnP solver directly estimates the transformation between the camera and the robot without training new networks or accessing mesh models. Evaluations in simulated and real-world benchmarks show that Kalib achieves good accuracy with a lower manual workload compared with recent baseline methods. We also demonstrate its application in multiple real-world settings with various robot arms and grippers. Kalib's user-friendly design and minimal setup requirements make it a possible solution for continuous operation in unstructured environments.
Paper Structure (27 sections, 3 equations, 7 figures, 2 tables)

This paper contains 27 sections, 3 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Hand-Eye Calibration
  4. Point Tracking with Foundation Models
  5. Method
  6. Overview
  7. Temporal Point Tracking
  8. Tracking Target
  9. Point Tracking Module
  10. Robot Kinematics
  11. Synchronization Mechanism
  12. Camera Pose Estimation
  13. Eye-in-Hand Setup
  14. Experiment
  15. Experimental Setup
  16. ...and 12 more sections

Figures (7)

  • Figure 1: Hand-eye calibration estimates the transform T between the camera and the robot. The proposed method can solve both eye-on-base (EoB) and eye-in-hand (EiH) calibration by tracking a predefined reference point. It's designed to work with minimal manual effort in household and unstructured environments, featuring an automatic pipeline and self-contained setup.
  • Figure 2: The whole pipeline starts by defining a reference point on the kinematic chain (Sec. \ref{['sec:method_tracking_target']}). The reference point tracking module can track its 2D position in the image frame (Sec. \ref{['sec:method_point_tracking_module']}), while its 3D coordinates in the robot frame can be derived by forward kinematics (Sec. \ref{['sec:method_robot_kinematics']}). Here the synchronization frame mechanism is introduced to balance precision and efficiency (Sec. \ref{['sec:method_sync']}). Finally, the PnP module can estimate the camera-to-robot transformation matrix, either $\mathbf{T}^{CE}$ for the eye-in-hand setting (Sec. \ref{['sec:method_pnp']}) or $\mathbf{T}^{CB}$ for the eye-on-base setting (Sec. \ref{['sec:method_eih']}).
  • Figure 3: Qualitative results in the real world. We draw masks of the robot projected onto the camera frame with our method in red. The precise fits of the mask and the robot suggest an accurate calibration result. Our method works under various settings, for example, (a) with a dual-arm robot and an egocentric camera, (b) with dexterous hand, (c) under the eye-in-hand setting, (d) when the robot is only partially visible, and (e) when the background is noisy. (f): When traditional methods fail, indicated by the green masks, our method can work as a post-doc remedy thanks to its markerless nature. (g): EasyHec (in blue masks) works well with a full view of the robot but may fail with a partial view. Our method can work in both conditions.
  • Figure 4: Error of tracking over the number of frames in simulation.
  • Figure 5: Translational and rotational error over the number of frames.
  • ...and 2 more figures