Motion model transitions in GPS-IMU sensor fusion for user tracking in augmented reality

TL;DR

This paper explores various motion models in GPS-IMU sensor fusion to improve user tracking accuracy in augmented reality, demonstrating that a dynamic model reduces errors and prevents divergence compared to static models.

Contribution

It introduces a DFA-based approach to select motion models dynamically, enhancing Kalman filter performance in AR user tracking.

Findings

Reduced filter error with the proposed method

Prevention of filter divergence

Validated on an AR game scenario

Abstract

Finding the position of the user is an important processing step for augmented reality (AR) applications. This paper investigates the use of different motion models in order to choose the most suitable one, and eventually reduce the Kalman filter errors in sensor fusion for such applications where the accuracy of user tracking is crucial. A Deterministic Finite Automaton (DFA) was employed using the innovation parameters of the filter. Results show that the approach presented here reduces the filter error compared to a static model and prevents filter divergence. The approach was tested on a simple AR game in order to justify the accuracy and performance of the algorithm.