RIDI: Robust IMU Double Integration

TL;DR

This paper introduces a data-driven inertial navigation method using IMU data from smartphones, leveraging human motion patterns to estimate trajectories with accuracy comparable to visual-inertial systems.

Contribution

It presents the first integration of machine learning with inertial navigation, learning to correct biases and estimate positions solely from IMU data.

Findings

Achieves comparable accuracy to visual-inertial navigation.

Works across multiple human subjects and phone placements.

Provides publicly available code and data for further research.

Abstract

This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research.