The field of state estimation is witnessing significant developments, with a growing focus on geometric and invariant approaches. Researchers are exploring ways to adapt traditional filtering methods, such as the Extended Kalman Filter, to non-Euclidean spaces and manifold geometries. This shift enables more accurate and robust state estimation in various applications, including aerospace and autonomous systems. Notably, the incorporation of geometric structures like affine connections and invariant theory is leading to improved filter designs. These advancements have the potential to enhance the performance of state estimation algorithms in complex and dynamic environments. Noteworthy papers include: The Geometry of Extended Kalman Filters on Manifolds with Affine Connection, which proposes a geometrically adapted EKF. Equivariant Filter for Relative Attitude and Target Angular Velocity Estimation, which develops an equivariant filter for estimating relative attitude and angular velocity. Quadratic Extended and Unscented Kalman Filter Updates, which introduces a quadratic approximation to improve estimation accuracy. The Invariant Zonotopic Set-Membership Filter for State Estimation on Groups, which extends invariant filtering theory to non-statistical settings. Invariant Extended Kalman Filter for Autonomous Surface Vessels with Partial Orientation Measurements, which applies invariant filtering to ASV state estimation with partial orientation measurements.