  Advances in micro-electro-mechanical (MEMS) techniques enable inertial
measurements units (IMUs) to be small, cheap, energy efficient, and widely used
in smartphones, robots, and drones. Exploiting inertial data for accurate and
reliable navigation and localization has attracted significant research and
industrial interest, as IMU measurements are completely ego-centric and
generally environment agnostic. Recent studies have shown that the notorious
issue of drift can be significantly alleviated by using deep neural networks
(DNNs), e.g. IONet. However, the lack of sufficient labelled data for training
and testing various architectures limits the proliferation of adopting DNNs in
IMU-based tasks. In this paper, we propose and release the Oxford Inertial
Odometry Dataset (OxIOD), a first-of-its-kind data collection for
inertial-odometry research, with all sequences having ground-truth labels. Our
dataset contains 158 sequences totalling more than 42 km in total distance,
much larger than previous inertial datasets. Another notable feature of this
dataset lies in its diversity, which can reflect the complex motions of
phone-based IMUs in various everyday usage. The measurements were collected
with four different attachments (handheld, in the pocket, in the handbag and on
the trolley), four motion modes (halting, walking slowly, walking normally, and
running), five different users, four types of off-the-shelf consumer phones,
and large-scale localization from office buildings. Deep inertial tracking
experiments were conducted to show the effectiveness of our dataset in training
deep neural network models and evaluate learning-based and model-based
algorithms. The OxIOD Dataset is available at: http://deepio.cs.ox.ac.uk

Robotics;Computer Vision and Pattern Recognition;Machine Learning