Improved Virtual Anchor Selection for AR-assisted Sensor Positioning in Harsh Indoor Conditions

In this work, we propose a sensor localization system assisted by wireless communication and augmented reality (AR) suitable for harsh indoor environments. Future handheld and unattended devices will be equipped with technologies, which have the means to enable localization of various phenomenon in indoor environments. These include visual odometry based on cameras and augmented reality, and communication hardware such as UWB. Integration of such technologies allows us to compensate for each other's errors in measurements. However, existing work cannot fully exploit these technologies to high extent, often inducing more errors or wasted resources. Furthermore, many other existing indoor localization methods require a pre-defined architecture or building information, which are impossible to acquire in harsh indoor conditions. In our proposed system, we specifically propose an improved method of utilizing virtual anchors for localization of sensor tags in harsh indoor environments, based on a cluster-based selection algorithm that allows the system to improve the positioning accuracy through angular diversity while also improving resource utilization without existing knowledge of the location. Our work has been fully implemented and tested on several indoor environments.