Joint Calibration

Joint calibration focuses on simultaneously calibrating multiple sensors or parameters within a system to improve overall accuracy and reliability. Current research emphasizes data-driven approaches, employing deep neural networks and other machine learning models, as well as techniques like simultaneous localization and mapping (SLAM) and Kalman filtering, to achieve robust and efficient calibration across diverse applications. This work is crucial for improving the performance of various systems, including robotic surgery, autonomous vehicles, and audio processing, where precise sensor alignment and parameter estimation are essential for accurate operation and reliable decision-making. The development of more accurate and efficient joint calibration methods directly impacts the safety and effectiveness of these technologies.