Robot Arm Self-Calibration Using RGB-D Camera

Kinematic and hand-eye calibration of robotic arms is a critical research area in robotics, essential to ensuring the accuracy of manipulation tasks. The widely adopted methods for robotic arm calibration typically rely on specialized markers or external sensors to achieve precise measurements. However, these approaches are often expensive and require additional effort, such as the installation and maintenance of auxiliary equipment. Furthermore, many downstream tasks require separate hand-eye calibration steps because of differences between the sensors used for calibration and those used for task execution. Comprehensive calibration of both the robot arm and sensors plays a vital role in optimizing system performance. However, the robot's posture could be constrained due to either the sensor's limited range or textureless scenes when a camera is used. To address these limitations, this study proposes a cost-effective self-calibration method that simultaneously calibrates the robot arm and determines the spatial relationship between the robot and an RGB-D camera, allowing for data collection at multiple locations. The proposed approach leverages recent advancements in machine learning to identify correspondences between images captured at different robot postures, facilitating automatic data selection. Furthermore, the removal of location constraints increases flexibility, enabling the collection of sufficient data as the robot's location changes. The method is evaluated using a Franka Emika Panda robotic arm, and the experimental results demonstrate its effectiveness in achieving accurate calibration without the need for external devices or markers.