  Accurate state and uncertainty estimation is imperative for mobile robots and
self driving vehicles to achieve safe navigation in pedestrian rich
environments. A critical component of state and uncertainty estimation for
robot navigation is to perform robustly under out-of-distribution noise.
Traditional methods of state estimation decouple perception and state
estimation making it difficult to operate on noisy, high dimensional data.
Here, we describe an approach that combines the expressiveness of deep neural
networks with principled approaches to uncertainty estimation found in
recursive filters. We particularly focus on techniques that provide better
robustness to out-of-distribution noise and demonstrate applicability of our
approach on two scenarios: a simple noisy pendulum state estimation problem and
real world pedestrian localization using the nuScenes dataset. We show that our
approach improves state and uncertainty estimation compared to baselines while
achieving approximately 3x improvement in computational efficiency.
