mlsanityData to 14 Oct 2026

Deep Learning for Inertial Positioning: A Survey

arXiv 2303.03757 ↗First posted 7 Mar 2023cs.RO cs.CVcited by 21

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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.

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

46 of 141 references.

References · 46

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34 more
  1. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 508
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 12; 2026: 7.

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  1. 2023Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directionscited by 21
  2. 2025MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigationcited by 3
  3. 2025On Neural Inertial Classification Networks for Pedestrian Activity Recognitioncited by 2
  4. 2025QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systemscited by 1
  5. 2025WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioningcited by 1
  6. 2026PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platformscited by 1
  7. 2024Data-Driven Gyroscope Calibrationcited by 0
  8. 2025Diffusion Denoiser-Aided Gyrocompassingcited by 0
  9. 2025A Survey on Deep Multi-Task Learning in Connected Autonomous Vehiclescited by 0
  10. 2025ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigationcited by 0
  11. 2025X-IONet: Cross-Platform Inertial Odometry Network for Pedestrian and Legged Robotcited by 0
  12. 2025Orientation-Free Neural Network-Based Bias Estimation for Low-Cost Stationary Accelerometerscited by 0
9 more
  1. 2025FDIO: Frequency Decomposed Inertial Odometrycited by 0
  2. 2025Dual-Agent Reinforcement Learning for Adaptive and Cost-Aware Visual-Inertial Odometrycited by 0
  3. 2025ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAMcited by 0
  4. 2026UMLoc: Uncertainty-Aware Map-Constrained Inertial Localization with Quantified Boundscited by 0
  5. 2026Neural-Assisted in-Motion Self-Heading Alignmentcited by 0
  6. 2026Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environmentscited by 0
  7. 2026Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learningcited by 0
  8. 2026Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensorscited by 0
  9. 2026Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mambacited by 0