TLIO: Tight Learned Inertial Odometry

TL;DR

This paper introduces a neural network-based method for 3D displacement estimation from IMU data, integrated into a Kalman filter for improved inertial odometry accuracy in pedestrian tracking.

Contribution

It presents a novel neural network that estimates 3D displacement and uncertainty, enabling tight fusion with an EKF for enhanced inertial odometry.

Findings

Network trained on pedestrian data produces consistent measurements.

Tightly-coupled system outperforms traditional velocity integration.

Improves position and orientation estimates in inertial odometry.

Abstract

In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU kinematic motion model. However the integration of measurements is sensitive to sensor bias and noise, causing significant drift within seconds. Recent research by Yan et al. (RoNIN) and Chen et al. (IONet) showed the capability of using trained neural networks to obtain accurate 2D displacement estimates from segments of IMU data and obtained good position estimates from concatenating them. This paper demonstrates a network that regresses 3D displacement estimates and its uncertainty, giving us the ability to tightly fuse the relative state measurement into a stochastic cloning EKF to solve for pose, velocity and sensor biases. We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty to be used as the update step in the filter, and the tightly-coupled system outperforms velocity integration approaches in position estimates, and AHRS attitude filter in orientation estimates.