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Conformalized Multimodal Uncertainty Regression and Reasoning
This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning regressor.
Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge
In the expanding landscape of AI-enabled robotics, robust quantification of predictive uncertainties is of great importance.
Lightweight, Uncertainty-Aware Conformalized Visual Odometry
Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics, and recent progress in the field has produced highly accurate point predictions in complex environments.
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies
We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning.
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