Evaluating Augmented Reality Landmark Cues and Frame of Reference Displays with Virtual Reality

Daily travel usually demands navigation on foot across a variety of different application domains, including tasks like search and rescue or commuting. Head-mounted augmented reality (AR) displays provide a preview of future navigation systems on foot, but designing them is still an open problem. In this paper, we look at two choices that such AR systems can make for navigation: 1) whether to denote landmarks with AR cues and 2) how to convey navigation instructions. Specifically, instructions can be given via a head-referenced display (screen-fixed frame of reference) or by giving directions fixed to global positions in the world (world-fixed frame of reference). Given limitations with the tracking stability, field of view, and brightness of most currently available head-mounted AR displays for lengthy routes outdoors, we decided to simulate these conditions in virtual reality. In the current study, participants navigated an urban virtual environment and their spatial knowledge acquisition was assessed. We experimented with whether or not landmarks in the environment were cued, as well as how navigation instructions were displayed (i.e., via screen-fixed or world-fixed directions). We found that the world-fixed frame of reference resulted in better spatial learning when there were no landmarks cued; adding AR landmark cues marginally improved spatial learning in the screen-fixed condition. These benefits in learning were also correlated with participants' reported sense of direction. Our findings have implications for the design of future cognition-driven navigation systems.

Automated summary

This study examines the design problem of augmented reality (AR) systems in pedestrian navigation by simulating real-world conditions in virtual reality. The results show that using the world-fixed frame of reference for navigation without landmark cues enhances spatial learning, while adding AR landmark cues in the screen-fixed condition slightly improves spatial learning. These findings have implications for the design of future cognition-driven navigation systems.