ARQuake: the outdoor augmented reality gaming system
Communications of the ACM - Internet abuse in the workplace and Game engines in scientific research
Orientation Tracking for Outdoor Augmented Reality Registration
IEEE Computer Graphics and Applications
Position-Annotated Photographs: A Geotemporal Web
IEEE Pervasive Computing
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses
WiNTECH '06 Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
Capturing indoor scenes with smartphones
Proceedings of the 25th annual ACM symposium on User interface software and technology
Hi-index | 0.00 |
Heading information becomes widely used in ubiquitous computing applications for mobile devices. Digital magnetometers, also known as geomagnetic field sensors, provide absolute device headings relative to the earth's magnetic north. However, magnetometer readings are prone to significant errors in indoor environments due to the existence of magnetic interferences, such as from printers, walls, or metallic shelves. These errors adversely affect the performance and quality of user experience of the applications requiring device headings. In this paper, we propose Headio, a novel approach to provide reliable device headings in indoor environments. Headio achieves this by aggregating ceiling images of an indoor environment, and by using computer vision-based pattern detection techniques to provide directional references. To achieve zero-configured and energy-efficient heading sensing, Headio also utilizes multimodal sensing techniques to dynamically schedule sensing tasks. To fully evaluate the system, we implemented Headio on both Android and iOS mobile platforms, and performed comprehensive experiments in both small-scale controlled and large-scale public indoor environments. Evaluation results show that Headio constantly provides accurate heading detection performance in diverse situations, achieving better than 1 degree average heading accuracy, up to 33X improvement over existing techniques.