Kalib: Easy Hand-Eye Calibration with Reference Point Tracking

2024-08-20

AI Summary

Conventional hand-eye calibration relies on artificial markers, while deep learning approaches require task-specific retraining or precise 3D CAD models—limiting adaptability in unstructured environments. Method: We propose a marker-free, training-free, and mesh-free calibration framework that requires only the robot’s kinematic model and a single predefined reference point. Leveraging vision foundation models (e.g., ViT/CLIP) for feature extraction and monocular tracking, our method jointly exploits forward kinematics and EPnP to estimate the hand-eye transformation. Contribution/Results: This is the first work to harness the zero-shot generalization capability of vision foundation models for hand-eye calibration, eliminating dependence on custom networks, calibration boards, or CAD models. The method enables continuous operation in unstructured settings. Extensive simulations and real-world experiments demonstrate accuracy competitive with state-of-the-art methods and reduce human intervention by over 50%. Deployed across multiple robotic arms and gripper platforms, it validates robustness and plug-and-play usability.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Estimates camera-robot transformation without fiducial markers
• Reduces manual effort and setup complexity in calibration
• Eliminates need for retraining networks or mesh models

Innovation

• Uses visual foundation models for generalization
• Tracks reference point for automatic calibration
• Requires only kinematic chain and reference point