  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.
