The gradual maturation of mobile augmented reality (AR) and localization technologies is enabling the development of immersive AR-enabled indoor localization and navigation systems. Existing indoor localization technologies (e.g., WiFi, infrared, Bluetooth) and navigation services do not provide intuitive 3D AR experiences and can be expensive to deploy. This paper introduces WebARNav, a cross-platform indoor localization system that provides user-friendly AR navigation services with low overhead and remarkable accuracy. First, we propose a lightweight location fusion framework for indoor navigation on the mobile web, which leverages accurate edge-supported vision localization to guide and correct lightweight pedestrian dead reckoning localization. Second, we improve the accuracy of localization using an attention-based feature extraction method and a dual-stream retrieval and co-visibility re-ranking technique for initial localization. Third, we significantly improve accuracy and speed up retrieval as users move by generating a topological map for traveling localization. We conducted extensive experiments on various indoor datasets to demonstrate localization accuracy and navigation experience. The study shows that WebARNav achieves a localization frequency of over 30 Hz and reduces the average trajectory error by 76% and 95% for single- and multi-floor office scenes, respectively, compared to the PDR-only method. The proposed traveling localization method also reduces the localization latency by 15.2%, 55.1%, and 98.6% in the baseline datasets, with an accuracy improvement of over 4%.

Web-based extended reality (WebXR) can enable lightweight, easy-to-access, and cross-platform augmented reality (AR) experiences. Context awareness is one key feature of AR. Supporting this in browser-based WebXR applications is challenging as typical object detection algorithms are too computationally demanding to be run in-browser, leading to slow response times and decreased battery life. In this demo, we show a WebXR AR application that uses a technique of WebRTC-based video streaming to obtain a usable video stream on an edge server to perform object detection.

Augmented Reality (AR) provides immersive user experiences by overlaying digital information on physical environments. Context-awareness is crucial for delivering relevant augmentations that best suit users' requirements and their en-vironments. In this article, we combine context-aware reasoning with emerging AR applications to provide the most relevant infor-mation according to user and environment contexts. To support the best possible quality of experience, 5G edge computing enables the distribution of computation-intensive AR tasks to edge servers through 5G networks. We develop ConAR, a context-aware head-mounted display AR system that is deployed on the edge and cloud leveraging both environmental sensors and user profile context for navigation. ConAR is composed of a HoloLens application and a paired mobile client, which contains a context model for air quality forecasting, and rendering recommendations on holograms through a HoloLens 2 device. We evaluate our system performance by deploying our proposed air quality prediction algorithm on the edge and cloud while communicating to them using 5G and LTE connections. We measure network quality metrics and find the deployment on the edge with 5G connections significantly outperforms alternative solutions. Our results demonstrate that the 5G edge computing is suitable for supporting latency-sensitive analysis tasks for context-aware AR.

Mobile Augmented Reality (MAR) mixes physical environments with user-interactive virtual annotations. Immersive MAR experiences are supported by computation-intensive tasks, which are typically offloaded to cloud or edge servers. Such offloading introduces additional network traffic and influences the motion-to-photon latency (a determinant of user-perceived quality of experience). Therefore, proper multimedia protocols are crucial to minimise transmission latency and ensure sufficient throughput to support MAR performance. Relatedly, 5G is a potential MAR supporting technology and is widely believed to be faster and more efficient than its predecessors. However, the suitability and performance of existing multimedia protocols for MAR in the 5G edge context have not been explored. In this work, we present a detailed evaluation of several popular multimedia protocols (HLS, MPEG-DASH, RTP, RTMP, RTMFP, and RTSP) and transport protocols (QUIC, UDP, and TCP) with a MAR system on a real-world 5G edge testbed. The evaluation results indicate that RTMP has the lowest median client-to-server packet latency on 5G and LTE for all image resolutions. In terms of individual image resolutions, from 144p to 480p over 5G and LTE, RTMP has the lowest median packet latency of $14.03\pm 1.05 {\mathrm ms}$. Whereas for jitter, HLS has the smallest median jitter across all image resolutions over LTE and 5G with medians of 2.62 ms and 1.41 ms, respectively. Our experimental results indicate that RTMP and HLS are the most suitable protocols for MAR.

Mobile Augmented Reality (MAR) provides a unique experience where the physical world is augmented with virtual annotations. MAR involves computation-heavy algorithms that could potentially be offloaded to edge servers on 5G networks, which significantly enhances MAR with reduced communication latency and more stable network connections, therefore leading to seamless MAR user experiences. In this demo, we show a running MAR system deployed on a 5G edge test bed and present latency results.