Self Calibration

Self-calibration encompasses techniques that automatically determine the parameters of a system or sensor without external reference points or manual intervention. Current research focuses on developing robust and efficient self-calibration methods across diverse applications, including camera-LiDAR systems, robotic manipulators, and eye-tracking devices, often employing techniques like iterative optimization, extended Kalman filters, and deep learning architectures. These advancements are crucial for improving the accuracy and autonomy of various systems, reducing reliance on laborious manual calibration procedures, and enabling wider deployment of technologies in challenging or dynamic environments. The resulting improvements in accuracy and efficiency have significant implications for fields such as robotics, autonomous driving, and medical imaging.