Hydra: Marker-Free RGB-D Hand-Eye Calibration

-- This work presents an RGB-D imaging-based approach to marker-free hand-eye calibration using a novel implementation of the iterative closest point (ICP) algorithm with a robust point-to-plane (PTP) objective formulated on a Lie algebra. Its applicability is demonstrated through comprehensive experiments using three well known serial manipulators and two RGB-D cameras. With only three randomly chosen robot configurations, our approach achieves approximately 90% successful calibrations, demonstrating 2 3 higher convergence rates to the global optimum compared to both marker-based and marker-free baselines. We also report 2 orders of magnitude faster convergence time ( 0 . Our method exhibits significantly improved accuracy ( 5 mm in task space) over classical approaches ( 7 mm in task space) whilst being marker-free. The benchmarking dataset and code are open sourced under Apache 2.0 License, and a ROS 2 integration with robot abstraction is provided to facilitate deployment Hand-eye calibration - determining a camera's pose relative to a serial manipulator and thus relating joint space to a Cartesian reference space - is a core component of robotics and computer vision, enabling autonomous operation and structured interaction with the environment.