RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods

TL;DR

This paper introduces a comprehensive benchmark, novel neural architectures, and evaluations for inertial navigation, significantly advancing data-driven position and orientation estimation from IMU data in natural human motions.

Contribution

It provides a new large-scale benchmark dataset, innovative neural network architectures, and thorough evaluations, fostering progress in neural inertial navigation research.

Findings

New benchmark with 40+ hours of IMU data from 100 subjects

Novel neural architectures improve navigation accuracy in challenging motions

Extensive evaluations demonstrate state-of-the-art performance

Abstract

This paper sets a new foundation for data-driven inertial navigation research, where the task is the estimation of positions and orientations of a moving subject from a sequence of IMU sensor measurements. More concretely, the paper presents 1) a new benchmark containing more than 40 hours of IMU sensor data from 100 human subjects with ground-truth 3D trajectories under natural human motions; 2) novel neural inertial navigation architectures, making significant improvements for challenging motion cases; and 3) qualitative and quantitative evaluations of the competing methods over three inertial navigation benchmarks. We will share the code and data to promote further research.