用手机陀螺仪数据精准估速，提升导航与交通应用体验。
An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU
- 通过噪声补偿与姿态对齐网络提升传感器精度。
- 在真实数据集上实现高精度速度估计，显著优于基线方法。
- 适合移动导航、智能交通等需要低成本高精度定位的场景。
通过智能手机准确估计车辆速度对移动导航和交通应用至关重要。本文提出一种融合时间学习模型的先进速度估计框架，利用惯性测量单元（IMU）数据，并由全球导航卫星系统（GNSS）信息监督。该框架包含噪声补偿网络，用于拟合传感器测量值与实际运动之间的噪声分布；以及姿态估计网络，用于对齐手机与车辆坐标系。为增强模型泛化能力，提出一种模拟车内不同手机放置方式的数据增强技术。此外，设计了一种新损失函数，有效缓解GNSS与IMU信号间的时序偏差，改善信号对齐，从而提升速度估计精度。最后，实现了一个高效原型，并在真实世界众包数据集上进行了广泛实验，结果表明该方法在准确性和效率方面均表现优异。
原文摘要 · Abstract (English)
Accurately estimating vehicle velocity via smartphone is critical for mobile navigation and transportation. This paper introduces a cutting-edge framework for velocity estimation that incorporates temporal learning models, utilizing Inertial Measurement Unit (IMU) data and is supervised by Global Navigation Satellite System (GNSS) information. The framework employs a noise compensation network to fit the noise distribution between sensor measurements and actual motion, and a pose estimation network to align the coordinate systems of the phone and the vehicle. To enhance the model's generalizability, a data augmentation technique that mimics various phone placements within the car is proposed. Moreover, a new loss function is designed to mitigate timestamp mismatches between GNSS and IMU signals, effectively aligning the signals and improving the velocity estimation accuracy. Finally, we implement a highly efficient prototype and conduct extensive experiments on a real-world crowdsourcing dataset, resulting in superior accuracy and efficiency.
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