  Visual-inertial odometry (VIO) is a vital technique used in robotics,
augmented reality, and autonomous vehicles. It combines visual and inertial
measurements to accurately estimate position and orientation. Existing VIO
methods assume a fixed noise covariance for the inertial uncertainty. However,
accurately determining in real-time the noise variance of the inertial sensors
presents a significant challenge as the uncertainty changes throughout the
operation leading to suboptimal performance and reduced accuracy. To circumvent
this, we propose VIO-DualProNet, a novel approach that utilizes deep learning
methods to dynamically estimate the inertial noise uncertainty in real-time. By
designing and training a deep neural network to predict inertial noise
uncertainty using only inertial sensor measurements, and integrating it into
the VINS-Mono algorithm, we demonstrate a substantial improvement in accuracy
and robustness, enhancing VIO performance and potentially benefiting other
VIO-based systems for precise localization and mapping across diverse
conditions.
