论文标题
基于深度学习的行人惯性导航：方法，数据集和设备推理
Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference
论文作者
论文摘要
现代的惯性测量单元（IMU）小，便宜，节能，并且在智能设备和移动机器人中广泛使用。利用惯性数据进行准确可靠的行人导航支持是新兴贸易Internet应用程序和服务的关键组成部分。最近，将深度神经网络（DNN）应用于运动感应和位置估计，人们越来越感兴趣。但是，缺乏足够的标记数据进行培训和评估体系结构基准，这限制了在基于IMU的任务中的DNN的采用。在本文中，我们介绍并发布了牛津惯性探针数据集（OXIOD），这是一种基于深度学习的惯性导航研究的首个公共数据集，所有序列都具有细粒度的地面真相。此外，为了在边缘启用更有效的推断，我们提出了一个新颖的轻型框架，以从RAW IMU数据中学习和重建行人轨迹。广泛的实验显示了我们的数据集和方法在实现资源约束设备上精确的数据驱动的惯性导航方面的有效性。
Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this paper, we present and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research, with fine-grained ground-truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our dataset and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.
