ARC-Calib: Autonomous Markerless Camera-to-Robot Calibration via Exploratory Robot Motions
This addresses the need for efficient and generalizable calibration in vision-based robot manipulation, offering an incremental improvement over existing autonomous methods by eliminating reliance on pre-trained models.
The paper tackles the problem of camera-to-robot calibration by proposing ARC-Calib, a fully autonomous and markerless framework that uses exploratory robot motions and geometric optimization, achieving robust performance across diverse robots and scenarios without requiring data collection or model training.
Camera-to-robot (also known as eye-to-hand) calibration is a critical component of vision-based robot manipulation. Traditional marker-based methods often require human intervention for system setup. Furthermore, existing autonomous markerless calibration methods typically rely on pre-trained robot tracking models that impede their application on edge devices and require fine-tuning for novel robot embodiments. To address these limitations, this paper proposes a model-based markerless camera-to-robot calibration framework, ARC-Calib, that is fully autonomous and generalizable across diverse robots and scenarios without requiring extensive data collection or learning. First, exploratory robot motions are introduced to generate easily trackable trajectory-based visual patterns in the camera's image frames. Then, a geometric optimization framework is proposed to exploit the coplanarity and collinearity constraints from the observed motions to iteratively refine the estimated calibration result. Our approach eliminates the need for extra effort in either environmental marker setup or data collection and model training, rendering it highly adaptable across a wide range of real-world autonomous systems. Extensive experiments are conducted in both simulation and the real world to validate its robustness and generalizability.