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

arXiv: https://arxiv.org/abs/2001.04061 · HTML: https://mlsanity.com/paper/2001.04061/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 16 of 16)

- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation](https://mlsanity.com/paper/2206.13603/) (2022)
- [A Hybrid Model and Learning-Based Adaptive Navigation Filter](https://mlsanity.com/paper/2207.12082/) (2022)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories](https://mlsanity.com/paper/2103.00262/) (2021)
- [Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2411.07828/) (2024)
- [Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement](https://mlsanity.com/paper/2401.05416/) (2023)
- [Deep Inertial Odometry with Accurate IMU Preintegration](https://mlsanity.com/paper/2101.07061/) (2021)
- [Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport](https://mlsanity.com/paper/2106.15178/) (2021)
- [DoorINet: Door Heading Prediction through Inertial Deep Learning](https://mlsanity.com/paper/2402.09427/) (2024)
- [Reliable Heading Tracking for Pedestrian Road Crossing Prediction Using Commodity Devices](https://mlsanity.com/paper/2410.06400/) (2024)
- [Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization](https://mlsanity.com/paper/2412.01767/) (2024)
- [Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2504.00438/) (2025)
- [ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation](https://mlsanity.com/paper/2510.19352/) (2025)
- [Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning](https://mlsanity.com/paper/2605.16391/) (2026)

## Abstract (arXiv)

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.