用神经网络动态预测噪声，提升无人车导航精度
A Hybrid Neural-Assisted Unscented Kalman Filter for Unmanned Ground Vehicle Navigation
- 用深度网络直接从原始传感器数据预测噪声不确定性
- 在三类车辆上实现位置误差降低12.7%
- 无需实测数据，可模拟训练后直接部署
现代无人地面车辆自主导航依赖多种估计算法融合惯性传感器与GNSS测量。然而，固定噪声协方差矩阵难以适应动态真实环境。本文提出一种混合估计框架，将经典状态估计算法与深度学习结合：不修改无迹卡尔曼滤波基本方程，而是通过专用深度神经网络，直接从原始惯性与GNSS数据预测过程与测量噪声不确定性。采用sim2real方法，仅在仿真数据上训练，获得完美真值并避免大量实地数据采集。为评估性能与泛化能力，使用来自三个数据集的160分钟测试集，涵盖越野车、乘用车、移动机器人三类平台，不同惯性传感器、路面与环境条件。结果表明，相比自适应模型方法，位置误差平均降低12.7%，展现出更强鲁棒性与可扩展性。
原文摘要 · Abstract (English)
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.
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