| Location is one of the key context to enable smart services for smartlife. Satellite based localization, e.g., GPS, has been one of the most important technological advances of the last half century. Current GPS-enabled smartphone has enabled a lot of mobile services to help us sense and interact with our surroundings, such as location-based services, maps, and navigation systems. All these applications have transformed our lives that we have never seen before. However, these could be just the tip of the iceberg for our demands of location sensing for future mobile smartlife applications, such as step-by-step navigation for the disabled, context-aware augmented-reality, fine-grained information recommendation, and wearable device tracking.;Current smartphone-based localization solutions are limited mainly to outdoor, mostly missing practical, robust and accurate indoor location solutions. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), e.g., step-by-step navigation for the Blind and visually impaired, there are several stringent requirements: highly accurate (foot-level); no additional hardware parts or extensions on users' smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. WiFi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. Existing services are seriously limited when applied in the more pervasive indoor environment due to low resolution. Things will get more complicated when using the low-complexity wearable devices for applications like augmenting the reality in malls or museums, and locating the group members. One of the biggest barriers to this fast emerging wearable engagement in general is the friction between the application needs and device limitations, i.e., physical size, power consumption, computational ability, and communication range. Adopting existing solutions directly to this new scenario is ineffective in accuracy and battery lifetime.;In this thesis, we propose enabling solutions for three representative smartlife applications, i.e., step-by-step navigation for the disabled, context-aware augmented-reality, and wearable device localization. The key technologies behind are fine-grained indoor localization, efficient and precise attitude and motion sensing, mobile crowd sensing. Specifically, we design and implement an indoor location ecosystem Guoguo. Guoguo consists of a constellation of low-complexity anchor network that enabling fine-grained localization with accuracy up to centimeter-level without any hardware/attachment burden on users' smartphones. We further propose opportunistic sensing approaches to improve its coverage, accuracy, and update rate. Our prototype shows centimeter-level location accuracy in several typical indoor environments. For applications when infrastructure is not available, we leverage crowds of smartphones for wearable localization via social collaboration and crowd sensing. Such precise and efficient sensing platform is anticipated to have high impact on the future smartlife applications and our daily activities.;To realize augmented reality (AR) applications under various scales and dynamics, we propose a suite of algorithms for fine-grained AR view tracking to improve the accuracy of attitude and displacement estimation, reduce the drift, get rid of the marker, and lower the computation cost. Instead of requiring extremely high accurate absolute locations, we offer multi-modal solutions according to mobility levels without additional hardware required. Experimental results show significantly less error in projecting and tracking the AR view. These results are expected to make users excited to explore their surroundings with enriched content. |
