mlsanityData to 7 Oct 2026

Neural Inertial Localization

arXiv 2203.15851 ↗First posted 29 Mar 2022cs.RO cs.CVcited by 13

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Abstract (arXiv)

This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements. This is an exciting and unexplored area of indoor localization research, where we present a rich dataset with 53 hours of inertial sensor data and the associated ground truth locations. We developed a solution, dubbed neural inertial localization (NILoc) which 1) uses a neural inertial navigation technique to turn inertial sensor history to a sequence of velocity vectors; then 2) employs a transformer-based neural architecture to find the device location from the sequence of velocities. We only use an IMU sensor, which is energy efficient and privacy preserving compared to WiFi, cameras, and other data sources. Our approach is significantly faster and achieves competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower. We share our code, model and data at https://sachini.github.io/niloc.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 11 of its 31 references that are in the map. First posted on arXiv 29 Mar 2022.

Builds on

11 of 31 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,479
  5. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  6. 2016LIFT: Learned Invariant Feature Transformcited by 305
  7. 2019RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methodscited by 72
  8. 2020TLIO: Tight Learned Inertial Odometrycited by 53
  9. 2019Scheduled Sampling for Transformerscited by 46
  10. 2021IDOL: Inertial Deep Orientation-Estimation and Localizationcited by 26
  11. 2021Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environmentscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 6; 2026: 1.

13 papers.

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  1. 2023Deep Learning for Inertial Positioning: A Surveycited by 20
  2. 2025AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observabilitycited by 6
  3. 2023Continuous Pose for Monocular Cameras in Neural Implicit Representationcited by 4
  4. 2022LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimationcited by 3
  5. 2023SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometrycited by 3
  6. 2025Learning IMU Bias with Diffusion Modelcited by 3
  7. 2023FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalizationcited by 2
  8. 2025Egocentric Action-aware Inertial Localization in Point Clouds with Vision-Language Guidancecited by 2
  9. 2024NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoTcited by 1
  10. 2025Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localizationcited by 1
  11. 2025GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna Systemcited by 1
  12. 2025IONext: Unlocking the Next Era of Inertial Odometrycited by 0
1 more
  1. 2026Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environmentscited by 0