Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference
This addresses a data bottleneck for researchers and developers in IoT and mobile robotics, enabling more efficient and accurate pedestrian navigation, though it is incremental in advancing existing deep learning applications.
The paper tackles the lack of labeled data for deep learning in pedestrian inertial navigation by releasing the Oxford Inertial Odometry Dataset (OxIOD) and proposes a lightweight framework for on-device inference, achieving accurate trajectory reconstruction on resource-constrained devices.
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.