Deep Inertial Odometry with Accurate IMU Preintegration

TL;DR

This paper introduces a deep inertial odometry method that leverages accurate IMU preintegration to improve motion estimation, combining model-driven and data-driven approaches for better performance.

Contribution

It proposes using accurate IMU preintegration within deep learning frameworks, enhancing inertial odometry accuracy over existing numerical approximation methods.

Findings

Validated improved accuracy of DIO with IMU preintegration

Demonstrated superiority over numerical approximation methods

Fusion of model-driven and data-driven approaches enhances robustness

Abstract

Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors. They are widely adopted in various autonomous systems. Motivated by the limitations in processing the noisy measurements from these sensors using their mathematical models, researchers have recently proposed various deep learning architectures to estimate inertial odometry in an end-to-end manner. Nevertheless, the high-frequency and redundant measurements from IMUs lead to long raw sequences to be processed. In this study, we aim to investigate the efficacy of accurate preintegration as a more realistic solution to the IMU motion model for deep inertial odometry (DIO) and the resultant DIO is a fusion of model-driven and data-driven approaches. The accurate IMU preintegration has the potential to outperform numerical approximation of the continuous IMU model used in the existing DIOs. Experimental results validate the proposed DIO.