Calibration Method

Calibration methods aim to improve the accuracy and reliability of various systems by correcting for inherent errors and uncertainties. Current research focuses on developing efficient and robust calibration techniques across diverse applications, including camera systems (employing novel patterns, bundle adjustment, and Gaussian processes), sensor networks (utilizing short-burst trials and data-driven lookup tables), and machine learning models (through techniques like self-consistency and class-wise calibration). These advancements are crucial for enhancing the precision and trustworthiness of numerous technologies, from robotics and autonomous systems to medical imaging and industrial automation.