Mobile Augmented Reality Framework with Fusional Localization and Pose Estimation

TL;DR

This paper introduces a new indoor mobile AR framework that combines fusional localization and pose estimation to enhance accuracy and performance over existing methods, reducing errors and improving user experience.

Contribution

The paper proposes a novel fusional localization and pose estimation framework for mobile AR, improving accuracy and matching rates compared to existing image or Wi-Fi based approaches.

Findings

Achieved low average error distances of 0.61-0.81m.

Attained accurate matching rates of 77%-82%.

Outperformed purely image or Wi-Fi based approaches.

Abstract

As a novel way of presenting information, augmented reality (AR) enables people to interact with the physical world in a direct and intuitive way. While there are some mobile AR products implemented with specific hardware at a high cost, the software approaches of AR implementation on mobile platforms(such as smartphones, tablet PC, etc.) are still far from practical use. GPS-based mobile AR systems usually perform poorly due to the inaccurate positioning in the indoor environment. Previous vision-based pose estimation methods need to continuously track predefined markers within a short distance, which greatly degrade user experience. This paper first conducts a comprehensive study of the state-of-the-art AR and localization systems on mobile platforms. Then, we propose an effective indoor mobile AR framework. In the framework, a fusional localization method and a new pose estimation implementation are developed to increase the overall matching rate and thus improving AR display accuracy. Experiments show that our framework has higher performance than approaches purely based on images or Wi-Fi signals. We achieve low average error distances (0.61-0.81m) and accurate matching rates (77%-82%) when the average sampling grid length is set to 0.5m.