提出融合定位与姿态估计的移动端AR框架，提升室内精度。
Mobile Augmented Reality Framework with Fusional Localization and Pose Estimation
- 融合视觉与信号定位，不依赖标记物或高成本硬件
- 平均误差0.61-0.81米，匹配率77%-82%
- 适合无标记环境下的移动AR应用开发
增强现实（AR）为信息呈现提供了新方式，使用户能以直观方式与物理世界互动。尽管已有部分基于专用硬件的移动AR产品，但基于手机、平板等平台的软件实现仍难以实用。基于GPS的移动AR系统在室内定位不准，表现不佳；以往基于视觉的姿态估计方法需近距离持续追踪预设标记，严重影响用户体验。本文首先对现有移动端AR与定位系统进行综合研究，随后提出一种有效的室内移动AR框架。该框架包含融合式定位方法与新型姿态估计实现，提升了整体匹配率，从而改善了AR显示精度。实验表明，本框架性能优于仅依赖图像或Wi-Fi信号的方法。当采样网格平均长度设为0.5米时，实现0.61-0.81米的低平均误差距离和77%-82%的准确匹配率。
原文摘要 · Abstract (English)
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.
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