State Estimation

State estimation aims to accurately determine the current state of a system—be it a robot, vehicle, or other dynamic entity—by fusing data from various sensors and incorporating models of system dynamics. Current research emphasizes robust and efficient algorithms, such as Kalman filters (including variations like Invariant Extended Kalman Filters and Moving Horizon Estimation), particle filters, and neural network-based approaches, to handle nonlinearity, sensor noise, and uncertainties in complex environments. These advancements are crucial for improving the performance and reliability of autonomous systems in diverse applications, ranging from robotics and autonomous driving to marine navigation and multi-agent coordination.