# RINS-W: Robust Inertial Navigation System on Wheels

arXiv: https://arxiv.org/abs/1903.02210 · HTML: https://mlsanity.com/paper/1903.02210/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 9 of 9)

- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation](https://mlsanity.com/paper/2205.07883/) (2022)
- [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025)
- [RIANN -- A Robust Neural Network Outperforms Attitude Estimation Filters](https://mlsanity.com/paper/2104.07391/) (2021)
- [Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6DoF Inertial Measurement Units](https://mlsanity.com/paper/2302.06037/) (2023)
- [Slip-Based Autonomous ZUPT through Gaussian Process to Improve Planetary Rover Localization](https://mlsanity.com/paper/2103.07587/) (2021)
- [Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models](https://mlsanity.com/paper/2204.06776/) (2022)
- [OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer](https://mlsanity.com/paper/2109.03091/) (2021)

## Abstract (arXiv)

This paper proposes a real-time approach for long-term inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses recurrent deep neural networks to dynamically detect a variety of situations of interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art Kalman filter which incorporates this knowledge as pseudo-measurements for localization. Evaluations on a publicly available car dataset demonstrates that the proposed scheme may achieve a final precision of 20 m for a 21 km long trajectory of a vehicle driving for over an hour, equipped with an IMU of moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is the first paper which combines sophisticated deep learning techniques with state-of-the-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial navigation techniques. Moreover, albeit taylored for IMU-only based localization, our method may be used as a component for self-localization of wheeled robots equipped with a more complete sensor suite.