# A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups

arXiv: https://arxiv.org/abs/2303.04747 · HTML: https://mlsanity.com/paper/2303.04747/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Multi-Camera Hand-Eye Calibration for Human-Robot Collaboration in Industrial Robotic Workcells](https://mlsanity.com/paper/2406.11392/) (2024)
- [Fast, Robust, Permutation-and-Sign Invariant SO(3) Pattern Alignment](https://mlsanity.com/paper/2512.00659/) (2025)

## Abstract (arXiv)

Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.