PlaneHEC: Efficient Hand-Eye Calibration for Multi-view Robotic Arm via Any Point Cloud Plane Detection

2025-07-26

AI Summary

Existing hand-eye calibration methods for multi-view robotic systems suffer from reliance on precise geometric models, manual intervention, and poor generalizability. To address these limitations, this paper proposes a universal, efficient calibration approach requiring only a depth camera and an arbitrary planar surface (e.g., a wall or tabletop). We formulate a novel hand-eye calibration equation grounded in planar geometric constraints, ensuring both interpretability and strong generalization across diverse scenes. Our method employs a hybrid solution strategy—combining a closed-form initialization with subsequent nonlinear optimization—to significantly enhance accuracy and robustness. The pipeline comprises three key stages: planar region detection from point clouds, rigid-body transformation estimation, and joint pose refinement. Extensive experiments in both synthetic and real-world environments demonstrate that our method outperforms state-of-the-art point-cloud-based approaches in both calibration accuracy and computational efficiency, achieving universal, fast, and high-precision hand-eye calibration.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Hand-eye calibration is an important task in vision-guided robotic systems and is crucial for determining the transformation matrix between the camera coordinate system and the robot end-effector. Existing methods, for multi-view robotic systems, usually rely on accurate geometric models or manual assistance, generalize poorly, and can be very complicated and inefficient. Therefore, in this study, we propose PlaneHEC, a generalized hand-eye calibration method that does not require complex models and can be accomplished using only depth cameras, which achieves the optimal and fastest calibration results using arbitrary planar surfaces like walls and tables. PlaneHEC introduces hand-eye calibration equations based on planar constraints, which makes it strongly interpretable and generalizable. PlaneHEC also uses a comprehensive solution that starts with a closed-form solution and improves it withiterative optimization, which greatly improves accuracy. We comprehensively evaluated the performance of PlaneHEC in both simulated and real-world environments and compared the results with other point-cloud-based calibration methods, proving its superiority. Our approach achieves universal and fast calibration with an innovative design of computational models, providing a strong contribution to the development of multi-agent systems and embodied intelligence.

Problem

• Hand-eye calibration for multi-view robotic systems
• Eliminates need for complex models or manual assistance
• Uses arbitrary planar surfaces for fast calibration

Innovation

• Uses arbitrary planar surfaces for calibration
• Combines closed-form and iterative optimization solutions
• Requires only depth cameras, no complex models