Example Concept: Sensor fusion in autonomous drones often employs Kalman filtering techniques to merge data from IMUs, GPS, cameras, and LiDAR. The Kalman filter provides a recursive solution to estimate the state of a dynamic system by predicting the state and updating it with new measurements, thereby reducing noise and improving accuracy. Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF) are adaptations that handle nonlinearities in sensor data, crucial for complex drone maneuvers and environmental interactions.