Smartphone-based indoor navigation services are desperately needed in indoor environments. However, the adoption of them has been relatively slow, due to the lack of fine-grained and up-to-date indoor maps, or the potentially high deployment and maintenance cost of infrastructure-based indoor localization solutions. This work proposes ViNav, a scalable and cost-efficient system that implements indoor mapping, localization, and navigation based on visual and inertial sensor data collected from smartphones. ViNav applies structure-from-motion (SfM) techniques to reconstruct 3D models of indoor environments from crowdsourced images, locates points of interest (POI) in 3D models, and compiles navigation meshes for path finding. ViNav implements image-based localization that identifies users' positions and facing directions, and leverages this feature to calibrate dead-reckoning-based user trajectories and sensor fingerprints collected along the trajectories. The calibrated information is utilized for building more informative and accurate indoor maps, and lowering the response delay of localization requests. According to our experimental results in a university building and a supermarket, the system works properly and our indoor localization achieves competitive performance compared with traditional approaches: in a supermarket, ViNav locates users within 2 seconds, with a distance error less than 1 meter and a facing direction error less than 6 degrees.

Visual crowdsourcing (VCS) offers an inexpensive method to collect visual data for implementing tasks, such as 3D mapping and place detection, thanks to the prevalence of smartphone cameras. However, without proper guidance, participants may not always collect data from desired locations with a required Quality-of-Information (QoI). This often causes either a lack of data in certain areas, or extra overheads for processing unnecessary redundancy. In this work, we propose SnapTask, a participatory VCS system that aims at creating complete indoor maps by guiding participants to efficiently collect visual data of high QoI. It applies Structure-from-Motion (SfM) techniques to reconstruct 3D models of indoor environments, which are then converted into indoor maps. To increase coverage with minimal redundancy, SnapTask determines locations for the next data collection tasks by analyzing the coverage of the generated 3D model and the camera views of the collected images. In addition, it overcomes the limitations of SfM techniques by utilizing crowdsourced annotations to reconstruct featureless surfaces (e.g. glass walls) in the 3D model. According to a field test in a library, the indoor map generated by SnapTask successfully reconstructs 100% of the library walls and 98.12% of objects and traversal areas within the library. With the same amount of input data our design of guided data collection increases the map coverage by 20.72% and 34.45%, respectively, compared with unguided participatory and opportunistic VCS.

This paper proposes a low-cost solution for mapping and locating UHF-band RFID tags in a 3D space using reader-equipped smartphones. Our solution includes a mobile augmented reality application for data collection and information visualization, and a cloud-based application server for calculating locations of the reader-equipped smartphones and the read RFID tags. Our solution applies computer vision and motion sensing techniques to track 3D locations of the RFID reader based on the visual and inertial sensor data collected from the companion smartphones. Meanwhile, it obtains the exact locations of RFID tags by calculating their relative positions from the readers based on the Angle of Arrival (AoA) concept. Our solution can be implemented with any low-cost fixed transmit power RFID readers, since it only requires the readers to report identifiers of read RFID tags. Furthermore, our solution does not require machine-controlled uniform movement of RFID readers, as it can handle the bias in the readings collected from randomly scattered positions. We have evaluated our solution with experiments in real environments using a commercially-off-the-shelf RFID reader and an Android phone. Results show that the average error in the positions of RFID tags is around 25cm for each of orthogonal axes on the floor plane, with the orders of RFID tags correctly detected in most cases.

An increasing number of online services are hosted on public clouds. However, since a centralized cloud architecture imposes high network latency, researchers suggested moving latency sensitive applications, such as virtual and augmented reality ones, to the edge of the network. Nevertheless, little has been done for edge layer capacity estimation resulting in a great need towards practical tools and techniques for initial capacity planning. In this work we provide a novel capacity planning solution for hierarchical edge cloud that considers QoS requirements in terms of response delay, and diverse demands for CPU, GPU and network resources. Our solution improves edge utilization by combining complementary resource demands while satisfying QoS requirements. We prove effectiveness of our solution through a case study where we plan edge capacity for deploying an AR navigation and information system.

The adoption of indoor navigation for smartphones has been relatively slow in the past years, although it would be direly needed in complex indoor areas. The primary barriers for its adoption include the lack of fine-grained and up-to-date indoor maps and the potential deployment and maintenance cost. In this paper we investigate the feasibility of utilizing crowdsourced data for building a smartphone-based indoor navigation system, focusing on the technical challenges caused by the varying quality of crowdsourced data. We developed iMoon, an indoor navigation system based on sensor-enriched 3D models of indoor environment, and evaluated its performance via a field study in a public building covering around 1,100 square meters.

The indoor location market is growing rapidly. However, fine-grained and up-to-date indoor maps are rarely available, and the existing indoor localization and navigation services mostly require extra infrastructures to provide accurate locations. In this demonstration we show iMoon, an indoor navigation system that provides indoor mapping, localization and navigation services using photos and sensor data collected from widely available mobile devices. The demonstration leverages a cohesive suite of computer vision, mobile sensing, and wireless networking techniques.