State Estimation Problem

State estimation aims to determine the current state of a system using noisy and potentially incomplete sensor data, a crucial task in robotics and numerous other fields. Current research focuses on improving robustness and efficiency in handling non-Gaussian noise, incomplete measurements, and high-dimensional state spaces, employing techniques like Bayesian filtering (including particle filters and Kalman filters), data-driven methods (e.g., neural networks and semi-supervised learning), and optimization-based approaches. These advancements are vital for enhancing the accuracy and reliability of autonomous systems, particularly in applications like navigation, object tracking, and sensor fusion, where precise state estimation is paramount.