# Neural Inertial Localization

arXiv: https://arxiv.org/abs/2203.15851 · HTML: https://mlsanity.com/paper/2203.15851/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [TLIO: Tight Learned Inertial Odometry](https://mlsanity.com/paper/2007.01867/) (2020)
- [Scheduled Sampling for Transformers](https://mlsanity.com/paper/1906.07651/) (2019)
- [IDOL: Inertial Deep Orientation-Estimation and Localization](https://mlsanity.com/paper/2102.04024/) (2021)
- [Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments](https://mlsanity.com/paper/2105.08837/) (2021)

## Built on by (most cited first, 13 of 13)

- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025)
- [Continuous Pose for Monocular Cameras in Neural Implicit Representation](https://mlsanity.com/paper/2311.17119/) (2023)
- [LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation](https://mlsanity.com/paper/2209.08816/) (2022)
- [SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry](https://mlsanity.com/paper/2304.01988/) (2023)
- [Learning IMU Bias with Diffusion Model](https://mlsanity.com/paper/2505.11763/) (2025)
- [FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization](https://mlsanity.com/paper/2303.06872/) (2023)
- [Egocentric Action-aware Inertial Localization in Point Clouds with Vision-Language Guidance](https://mlsanity.com/paper/2505.14346/) (2025)
- [NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoT](https://mlsanity.com/paper/2404.08939/) (2024)
- [Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization](https://mlsanity.com/paper/2504.07677/) (2025)
- [GNSS Jammer Direction Finding in Dynamic Scenarios Using an Inertial-based Multi-Antenna System](https://mlsanity.com/paper/2512.05128/) (2025)
- [IONext: Unlocking the Next Era of Inertial Odometry](https://mlsanity.com/paper/2507.17089/) (2025)
- [Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments](https://mlsanity.com/paper/2605.07412/) (2026)

## 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.