Mobile augmented reality (AR), which integrates virtual objects with 3D real environments in real time, has been showing outstanding potential in many application areas including education, retail, and healthcare. AR is broadly expected to redefine how we interact with the world around us. Yet current AR falls short of many of the expectations. This talk presents our vision of multi-device, edge computing-supported and Internet-of-Things (IoT)-integrated architectures for next-generation intelligent context-adaptive AR. The talk describes shortcomings in modern AR’s semantic and spatial awareness capabilities and identifies key research gaps that need to be addressed to enable AR to become robust and resource-efficient; we discuss solutions to some of the key challenges, based on the advances in edge computing, machine learning, and resource-efficient simultaneous localization and mapping. The talk also highlights the opportunities associated with the close integration of AR platforms and their users, and describes how integrated multi-device architectures can improve user context awareness in AR. The talk showcases several applications of next-generation context-aware AR, including AR in surgery and mental health.

This talk is based on research that appeared in ACM SenSys, IEEE ISMAR, ACM IMWUT, IEEE INFOCOM, and IEEE/ACM IPSN.