Sensor Fusion Framework

Sensor fusion frameworks integrate data from multiple sensors to improve the accuracy and robustness of perception systems in various applications, such as autonomous vehicles and robotics. Current research emphasizes developing unified architectures, often employing techniques like factor graphs, Kalman filters (including variations like the Maximum Correntropy Criterion Kalman Filter), and transformers, to efficiently fuse heterogeneous sensor data (e.g., LiDAR, cameras, radar, IMU) and handle intermittent or unreliable measurements. These advancements are crucial for enhancing the reliability and performance of autonomous systems operating in complex and dynamic environments, leading to safer and more efficient applications across diverse fields.