Efficient bundle optimization for accurate camera pose estimation in mobile augmented reality systems

Augmented reality has a long research history in computer vision and computer graphics communities. It aims to enhance the user experience for real scenes via overlapping virtual objects. Nowadays, mobile augmented reality has attracted much attention from researchers and developers due to the development of hardware techniques. Modern mobile devices such as mobile phones have a powerful computational ability for augmented reality applications. As a result, many researchers have paid attention to mobile augmented reality. From the technical viewpoint of augmented reality, mobile augmented reality largely depends on camera pose estimation. However, existing methods make it difficult to achieve the best balance between accuracy and efficiency, according to our investigation, and this may handicap the performance of mobile augmented reality systems. To overcome the problem, in this paper, we propose a novel approach to camera pose estimation based on bundle optimization. Our proposed method is evaluated on real-world datasets and is also tested in the mobile augmented reality system. Both experiments demonstrate that our proposed method has fast speed and high accuracy.