Modern autonomous navigation for unmanned ground vehicles relies on different estimators to fuse inertial sensors and GNSS measurements. However, the constant noise covariance matrices often struggle to account for dynamic real-world conditions. In this work we propose a hybrid estimation framework that bridges classical state estimation foundations with modern deep learning approaches. Instead of altering the fundamental unscented Kalman filter equations, a dedicated deep neural network is developed to predict the process and measurement noise uncertainty directly from raw inertial and GNSS measurements. We present a sim2real approach, with training performed only on simulative data. In this manner, we offer perfect ground truth data and relieves the burden of extensive data recordings. To evaluate our proposed approach and examine its generalization capabilities, we employed a 160-minutes test set from three datasets each with different types of vehicles (off-road vehicle, passenger car, and mobile robot), inertial sensors, road surface, and environmental conditions. We demonstrate across the three datasets a position improvement of $12.7\%$ compared to the adaptive model-based approach. Thus, offering a scalable and a more robust solution for unmanned ground vehicles navigation tasks.

翻译：现代无人地面车辆的自主导航依赖于不同估计器融合惯性传感器与全球导航卫星系统（GNSS）测量数据。然而，恒定噪声协方差矩阵往往难以适应动态变化的真实世界条件。本文提出一种混合估计框架，将经典状态估计基础与现代深度学习方法相衔接。该方法并非改变无迹卡尔曼滤波器的核心方程，而是通过专用深度神经网络直接从原始惯性测量与GNSS数据中预测过程噪声与测量噪声的不确定性。我们采用仿真到现实（sim2real）策略，仅使用模拟数据进行训练，从而提供完美的真实值数据并减轻大量实地数据采集的负担。为评估所提方法及其泛化能力，我们使用了来自三个数据集、总时长160分钟的测试集，这些数据集涵盖不同车辆类型（越野车、乘用车及移动机器人）、惯性传感器、路面条件及环境场景。实验表明，与基于自适应模型的方法相比，该方法在三个数据集上的定位精度提升了12.7%，为无人地面车辆导航任务提供了可扩展且更鲁棒的解决方案。