VIO-DualProNet：基于学习的过程噪声协方差的视觉惯性里程计
VIO-DualProNet: Visual-Inertial Odometry with Learning Based Process Noise Covariance
AI总结：
本文提出VIO-DualProNet，通过深度神经网络仅利用惯性测量实时估计惯性噪声协方差，并集成到VINS-Mono中，从而提升VIO的精度与鲁棒性。
摘要：
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，它结合视觉与惯性测量来准确估计位置和姿态。现有VIO方法通常为惯性不确定性假设固定的噪声协方差。然而，由于不确定性在整个运行过程中会发生变化，实时准确确定惯性传感器的噪声方差面临重大挑战，这会导致性能次优和精度下降。为解决这一问题，我们提出VIO-DualProNet，这是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新方法。通过设计并训练一个仅使用惯性传感器测量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，我们证明了精度和鲁棒性的显著提升，增强了VIO性能，并可能有益于其他基于VIO的系统在不同条件下实现精确定位与建图。
英文摘要：
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.
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