Sensor Calibration

Sensor calibration aims to accurately determine the relationship between sensor readings and real-world measurements, crucial for reliable data fusion and accurate system performance across diverse applications. Current research emphasizes developing efficient and robust calibration methods, particularly for autonomous vehicles and environmental monitoring, employing techniques like neural networks (including deep learning and variations like GRNNs and LSTMs), optimization algorithms (e.g., Kalman filters, least squares), and novel approaches using implicit neural representations and Gaussian splatting. These advancements improve the accuracy and reliability of sensor data, impacting fields ranging from autonomous driving and robotics to air quality monitoring and medical imaging.