# Deep Learning for Inertial Positioning: A Survey

arXiv: https://arxiv.org/abs/2303.03757 · HTML: https://mlsanity.com/paper/2303.03757/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 141 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Parallel WaveNet: Fast High-Fidelity Speech Synthesis](https://mlsanity.com/paper/1711.10433/) (2017)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [TLIO: Tight Learned Inertial Odometry](https://mlsanity.com/paper/2007.01867/) (2020)
- [VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem](https://mlsanity.com/paper/1701.08376/) (2017)
- [RIDI: Robust IMU Double Integration](https://mlsanity.com/paper/1712.09004/) (2017)
- [Selective Sensor Fusion for Neural Visual-Inertial Odometry](https://mlsanity.com/paper/1903.01534/) (2019)
- [Distilling Knowledge From a Deep Pose Regressor Network](https://mlsanity.com/paper/1908.00858/) (2019)
- [AI-IMU Dead-Reckoning](https://mlsanity.com/paper/1904.06064/) (2019)
- [DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints](https://mlsanity.com/paper/1906.11435/) (2019)
- [Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation](https://mlsanity.com/paper/2002.10718/) (2020)

## Built on by (most cited first, 21 of 21)

- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation](https://mlsanity.com/paper/2507.18206/) (2025)
- [On Neural Inertial Classification Networks for Pedestrian Activity Recognition](https://mlsanity.com/paper/2502.17520/) (2025)
- [QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems](https://mlsanity.com/paper/2503.01916/) (2025)
- [WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioning](https://mlsanity.com/paper/2503.13568/) (2025)
- [PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms](https://mlsanity.com/paper/2601.03040/) (2026)
- [Data-Driven Gyroscope Calibration](https://mlsanity.com/paper/2410.12485/) (2024)
- [Diffusion Denoiser-Aided Gyrocompassing](https://mlsanity.com/paper/2507.21245/) (2025)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation](https://mlsanity.com/paper/2510.19352/) (2025)
- [X-IONet: Cross-Platform Inertial Odometry Network for Pedestrian and Legged Robot](https://mlsanity.com/paper/2511.08277/) (2025)
- [Orientation-Free Neural Network-Based Bias Estimation for Low-Cost Stationary Accelerometers](https://mlsanity.com/paper/2511.13071/) (2025)
- [FDIO: Frequency Decomposed Inertial Odometry](https://mlsanity.com/paper/2511.15645/) (2025)
- [Dual-Agent Reinforcement Learning for Adaptive and Cost-Aware Visual-Inertial Odometry](https://mlsanity.com/paper/2511.21083/) (2025)
- [ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM](https://mlsanity.com/paper/2512.00037/) (2025)
- [UMLoc: Uncertainty-Aware Map-Constrained Inertial Localization with Quantified Bounds](https://mlsanity.com/paper/2601.06602/) (2026)
- [Neural-Assisted in-Motion Self-Heading Alignment](https://mlsanity.com/paper/2604.00168/) (2026)
- [Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments](https://mlsanity.com/paper/2605.07412/) (2026)
- [Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning](https://mlsanity.com/paper/2605.16391/) (2026)
- [Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors](https://mlsanity.com/paper/2607.05663/) (2026)
- [Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba](https://mlsanity.com/paper/2607.05669/) (2026)

## Abstract (arXiv)

Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization. Inertial sensor-based positioning is essential in various applications, including personal navigation, location-based security, and human-device interaction. However, low-cost MEMS inertial sensors' measurements are inevitably corrupted by various error sources, leading to unbounded drifts when integrated doubly in traditional inertial navigation algorithms, subjecting inertial positioning to the problem of error drifts. In recent years, with the rapid increase in sensor data and computational power, deep learning techniques have been developed, sparking significant research into addressing the problem of inertial positioning. Relevant literature in this field spans across mobile computing, robotics, and machine learning. In this article, we provide a comprehensive review of deep learning-based inertial positioning and its applications in tracking pedestrians, drones, vehicles, and robots. We connect efforts from different fields and discuss how deep learning can be applied to address issues such as sensor calibration, positioning error drift reduction, and multi-sensor fusion. This article aims to attract readers from various backgrounds, including researchers and practitioners interested in the potential of deep learning-based techniques to solve inertial positioning problems. Our review demonstrates the exciting possibilities that deep learning brings to the table and provides a roadmap for future research in this field.