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

Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies