用神经网络融合轮载惯性与GNSS数据，定位误差降低46%。
Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
- 通过周期性轨迹提升信号质量，神经网络仅凭惯性数据估位移
- 实测显示定位均方根误差降低约46%，优于传统融合方法
- 适合对精度要求高的自动驾驶车辆，尤其在GNSS弱信号场景
高精度且鲁棒的定位仍是自主地面车辆的核心挑战。本文提出一种混合神经惯性导航框架，融合轮载惯性传感器、强制周期性轨迹以及一个简单高效的神经网络，该网络能在误差状态扩展卡尔曼滤波中利用GNSS位置更新回归车辆位移。周期性轨迹提升了惯性信号信噪比，使网络仅依赖惯性读数即可估计位移。通过多组轮载惯性传感器的真实世界实验验证，结果表明该方法显著提升定位精度，相较于标准轮载惯性传感器与GNSS融合，位置均方根误差降低约46%。
原文摘要 · Abstract (English)
Accurate and robust localization remains a fundamental challenge for autonomous ground vehicles. In this work, we propose a hybrid neural inertial navigation framework that integrates a wheel-mounted inertial sensors, enforced periodic trajectories, and a simple, efficient neural network capable of regressing vehicle displacement with GNSS position updates in an error-state extended Kalman filter. The periodic trajectories increase the inertial signal-to-noise ratio, allowing the network to use only inertial readings to estimate displacement. The approach is validated through real-world experiments using multiple wheel-mounted inertial sensors. Experimental results demonstrate that the proposed method achieves a significant improvement in positioning accuracy, reducing the position root mean squared error by approximately 46 % compared to standard wheel-mounted inertial sensor fusion with GNSS updates.
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