# LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation

arXiv: https://arxiv.org/abs/1807.05275 · HTML: https://mlsanity.com/paper/1807.05275/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach](https://mlsanity.com/paper/2103.14286/) (2021)
- [Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation](https://mlsanity.com/paper/2105.00250/) (2021)
- [Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization](https://mlsanity.com/paper/2109.00126/) (2021)
- [OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer](https://mlsanity.com/paper/2109.03091/) (2021)

## Abstract (arXiv)

We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory (LSTM) neural network. While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user. Upon detection, zero-velocity pseudo-measurements are fused with a dead reckoning motion model in an extended Kalman filter (EKF). We demonstrate that our LSTM-based zero-velocity detector, used within a zero-velocity-aided INS, improves zero-velocity detection during human localization tasks. Consequently, localization accuracy is also improved. Our system is evaluated on more than 7.5 km of indoor pedestrian locomotion data, acquired from five different subjects. We show that 3D positioning error is reduced by over 34% compared to existing fixed-threshold zero-velocity detectors for walking, running, and stair climbing motions. Additionally, we demonstrate how our learned zero-velocity detector operates effectively during crawling and ladder climbing. Our system is calibration-free (no careful threshold-tuning is required) and operates consistently with differing users, IMU placements, and shoe types, while being compatible with any generic zero-velocity-aided INS.