Map Recovery and Fusion for Collaborative Augment Reality of Multiple Mobile Devices
Jianhua Zhang, Jialing Liu, Kaiqi Chen, Zhiying Pan, Ruyu Liu, Yanyan Wang, Thomas Yang, Shengyong Chen
Abstract
The map recovery and fusion is a key issue in the application of large scale and long-term augmented reality (AR) scenarios. However, they are still not addressed well in an efficient and precise way, especially for complex industrial environments. In this article, we propose a map recovery and fusion strategy based on vision-inertial simultaneous localization and mapping. We first develop a heuristic strategy that can fast search and match map points among multiple maps, and can be used for efficient map fusion. For map recovery, we leverage the inertial sensors for short time motion estimation, and transform the previous lost map to the current map. Based on this strategy, a novel framework for collaborative AR is implemented and can parallelly run in multiple mobile devices in real time. Extensive experiments have been carried out on a public data set, and the results show that the proposed method can recovery and fuse multiple maps with high completeness and precision.