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、增强现实和自动驾驶车辆中的一项关键技术。它融合视觉和惯性测量值，以精确估计位置和姿态。现有VIO方法假设惯性不确定性具有固定的噪声协方差。然而，在实际应用中，由于惯性传感器的噪声方差在运行过程中不断变化，准确确定其值构成重大挑战，从而导致性能欠佳和精度下降。为解决此问题，我们提出VIO-DualProNet，一种利用深度学习方法动态实时估计惯性噪声不确定性的新颖方法。通过设计并训练一个仅使用惯性传感器测量值来预测惯性噪声不确定性的深度神经网络，并将其集成到VINS-Mono算法中，我们证明了该方法在精度和鲁棒性上的显著提升，不仅增强了VIO性能，还有望惠及其他基于VIO的系统，使其在不同条件下实现精确定位与建图。