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.

翻译：视觉-惯性里程计（VIO）是机器人技术、增强现实和自动驾驶等领域中的关键技术。它通过融合视觉和惯性测量信息，精确估计位置和姿态。现有VIO方法假设惯性测量中的噪声协方差为固定值。然而，在实际运行过程中，惯性传感器的噪声方差会随工况变化而动态改变，如何实时准确确定该噪声方差仍是一项重大挑战，这会导致算法性能次优且精度下降。为解决此问题，我们提出VIO-DualProNet——一种利用深度学习方法实时动态估计惯性噪声不确定性的创新方案。通过设计并训练深度神经网络，仅使用惯性传感器测量值即可预测噪声不确定性，并将其集成至VINS-Mono算法中。实验表明，该方法显著提升了VIO的精度与鲁棒性，增强了VIO性能，并有望惠及其他基于VIO的系统，使其能在多样化环境下实现精准定位与建图。