LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation

TL;DR

This paper introduces an LSTM neural network-based zero-velocity detector that enhances inertial navigation accuracy across various human motions by robustly identifying stationary periods without manual threshold tuning.

Contribution

The novel contribution is the development of a learned zero-velocity detector using LSTM that outperforms traditional threshold-based methods in diverse motion scenarios.

Findings

Reduces 3D positioning error by over 34%

Operates effectively during crawling and ladder climbing

Calibration-free and robust across users and IMU placements

Abstract

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.