The field of state estimation and localization is witnessing significant advancements, driven by the need for accurate and reliable navigation in complex and dynamic environments. Researchers are exploring innovative approaches to address the challenges posed by real-world scenarios, such as underwater environments, vineyards, and GPS-denied areas. A key trend is the development of robust and adaptable filtering techniques, including Kalman filters and particle filters, that can effectively handle high-dimensional measurements and mitigate perceptual aliasing. Another area of focus is the integration of semantic information and bioinspired approaches to improve the accuracy and efficiency of localization systems. Notable papers in this area include:

• The Reversible Kalman Filter for state estimation on manifolds, which enables precise evaluation of existing Kalman filter variants with arbitrary accuracy on synthetic data.
• The Semantic-Aware Particle Filter for reliable vineyard robot localization, which incorporates stable object-level detections and semantic walls to mitigate row aliasing.
• The Extended Kalman Filter for systems with infinite-dimensional measurements, which provides a novel system-theoretic justification for the use of image gradients as features for vision-based state estimation.
• The Bioinspired SLAM Approach for unmanned surface vehicles, which delivers low-computation-cost visual-inertial based SLAM suitable for GPS-denied environments.