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Modern inertial measurements units (IMUs) are small, cheap, energy efficient,and widely employed in smart devices and mobile robots. Exploiting inertialdata for accurate and reliable pedestrian navigation supports is a keycomponent for emerging Internet-of-Things applications and services. Recently,there has been a growing interest in applying deep neural networks (DNNs) tomotion sensing and location estimation. However, the lack of sufficientlabelled data for training and evaluating architecture benchmarks has limitedthe adoption of DNNs in IMU-based tasks. In this paper, we present and releasethe Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind publicdataset for deep learning based inertial navigation research, with fine-grainedground-truth on all sequences. Furthermore, to enable more efficient inferenceat the edge, we propose a novel lightweight framework to learn and reconstructpedestrian trajectories from raw IMU data. Extensive experiments show theeffectiveness of our dataset and methods in achieving accurate data-drivenpedestrian inertial navigation on resource-constrained devices.

Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference