  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. However, emerging autonomous edge robotics
devices like insect-scale drones and surgical robots lack a computationally
efficient framework to estimate VO's predictive uncertainties. Meanwhile, as
edge robotics continue to proliferate into mission-critical application spaces,
awareness of model's the predictive uncertainties has become crucial for
risk-aware decision-making. This paper addresses this challenge by presenting a
novel, lightweight, and statistically robust framework that leverages conformal
inference (CI) to extract VO's uncertainty bands. Our approach represents the
uncertainties using flexible, adaptable, and adjustable prediction intervals
that, on average, guarantee the inclusion of the ground truth across all
degrees of freedom (DOF) of pose estimation. We discuss the architectures of
generative deep neural networks for estimating multivariate uncertainty bands
along with point (mean) prediction. We also present techniques to improve the
uncertainty estimation accuracy, such as leveraging Monte Carlo dropout
(MC-dropout) for data augmentation. Finally, we propose a novel training loss
function that combines interval scoring and calibration loss with traditional
training metrics--mean-squared error and KL-divergence--to improve
uncertainty-aware learning. Our simulation results demonstrate that the
presented framework consistently captures true uncertainty in pose estimations
across different datasets, estimation models, and applied noise types,
indicating its wide applicability.
