  We present a novel monocular localization framework by jointly training deep
learning-based depth prediction and Bayesian filtering-based pose reasoning.
The proposed cross-modal framework significantly outperforms deep learning-only
predictions with respect to model scalability and tolerance to environmental
variations. Specifically, we show little-to-no degradation of pose accuracy
even with extremely poor depth estimates from a lightweight depth predictor.
Our framework also maintains high pose accuracy in extreme lighting variations
compared to standard deep learning, even without explicit domain adaptation. By
openly representing the map and intermediate feature maps (such as depth
estimates), our framework also allows for faster updates and reusing
intermediate predictions for other tasks, such as obstacle avoidance, resulting
in much higher resource efficiency.
