ROS 2 development of an Extended Kalman Filter for state estimation of nano-quadrotors

In real-world scenarios, accurately estimating the state of dynamic systems under the influence of noise is crucial for effective control and monitoring. Extended Kalman Filtering (EKF) is a powerful tool that addresses this necessity by providing robust state estimation even in the presence of both state and measurement noises. EKF is particularly valuable because it can reconstruct the complete state of a system from measurements of only a subset of state variables. This thesis explores the application of the Extended Kalman Filter (EKF) for quadrotors, detailing its formulation and implementation. The discussion begins with the theory behind the EKF, then its performance is validated through extensive simulations conducted using Webots, a robot simulation platform. These simulations demonstrate the filter's effectiveness in accurately estimating quadrotor states despite noisy conditions.