Estimation Algorithm

Estimation algorithms aim to infer unknown parameters or states from observed data, a fundamental problem across diverse scientific fields. Current research emphasizes developing efficient and robust algorithms, focusing on areas like optimal linear-time solutions for pose estimation (e.g., using point and line features), handling highly non-uniform data sampling in matrix completion, and improving distributed estimation through techniques like consensus-based fusion and value-of-information censoring. These advancements have significant implications for applications ranging from robotics and computer vision (e.g., human pose estimation, visual-inertial SLAM) to precision agriculture and responsible AI, where accurate and efficient estimation is crucial.