论文标题
神经惯性定位
Neural Inertial Localization
论文作者
论文摘要
本文提出了惯性定位问题，即从一系列惯性传感器测量序列估算绝对位置的任务。这是室内本地化研究的一个令人兴奋且尚未开发的领域，我们在其中介绍了一个拥有53个小时的惯性传感器数据和相关地面真相位置的丰富数据集。我们开发了一种称为神经惯性定位（NILOC）的解决方案，1）使用神经惯性导航技术将惯性传感器历史转换为一系列速度向量；然后2）采用基于变压器的神经结构来从速度序列中找到设备位置。我们仅使用IMU传感器，该传感器与WiFi，相机和其他数据源相比可以节能和隐私保留。与需要平面图并慢20到30倍的最先进方法相比，我们的方法甚至可以达到竞争成果的速度明显更快，并且可以取得竞争性的结果。我们在https://sachini.github.io/niloc上共享我们的代码，模型和数据。
This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements. This is an exciting and unexplored area of indoor localization research, where we present a rich dataset with 53 hours of inertial sensor data and the associated ground truth locations. We developed a solution, dubbed neural inertial localization (NILoc) which 1) uses a neural inertial navigation technique to turn inertial sensor history to a sequence of velocity vectors; then 2) employs a transformer-based neural architecture to find the device location from the sequence of velocities. We only use an IMU sensor, which is energy efficient and privacy preserving compared to WiFi, cameras, and other data sources. Our approach is significantly faster and achieves competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower. We share our code, model and data at https://sachini.github.io/niloc.
