3D City Modeling and Visualization for Smart Phone Applications

Smartphones with larger screens, powerful processors, abundant memory, and an open operation system provide many possibilities for 3D city or photorealistic model applications. 3D city or photorealistic models can be used by the users to locate themselves in the 3D world, or they can be used as methods for visualizing the surrounding environment once a smartphone has already located the phone by other means, e.g. by using GNSS, and then to provide an interface in the form of a 3D model for the location-based services. In principle, 3D models can be also used for positioning purposes. For example, matching of images exported from the smartphone and then registering them in the existing 3D photorealistic world provides the position of the image capture. In that process, the central computer can do a similar image matching task when the users locate themselves interactively into the 3D world. As the benefits of 3D city models are obvious, this chapter demonstrates the technology used to provide photorealistic 3D city models and focus on 3D data acquisition and the methods available in 3D city modeling, and the development of 3D display technology for smartphone applications. Currently, global geoinformatic data providers, such as Google, Nokia (NAVTEQ), and TomTom (Tele Atlas), are expanding their products from 2D to 3D. This chapter is a presentation of a case study of 3D data acquisition, modeling and mapping, and visualization for a smartphone, including an example based on data collected by mobile laser scanning data from the Tapiola (Espoo, Finland) test field.

Location-Based Services and Navigation in Smart Phones

The ubiquitous positioning ability and abundant computation capability of a smart phone allow the provision of a variety of location-based services (LBSs). This chapter focuses on the fundamental elements and principles of LBS in a smart phone. First, the basic concept of LBS is introduced. Second, the state-of-the-art smart phones and communication networks are described. Afterwards, the smart phone positioning technologies are presented as three groups: satellite-based technologies, network-based solutions, and sensor-based approaches. Then, the location relevant services, contents, data, and context in a smart phone are explained. Furthermore, in the perspective of the new generation of LBS, the emerging features and technical solutions are discussed. Finally, three examples show how the above elements are integrated into the LBS applications in a smart phone.

RFID Positioning

This chapter investigates the techniques and algorithms for low-cost and portable ubiquitous indoor/outdoor personal positioning systems that use Radio Frequency Identification (RFID) based multi-sensor integration. This includes, for example, integrating RFID with Micro-Electro-Mechanical Systems (MEMS), Inertial Navigation System (INS), and/or GNSS. Different positioning scenarios and technologies are discussed and assessed. Their principles of operation and the factors that affect signal strength measurements are analysed in detail. This is followed by an in-depth investigation of the achievable positioning accuracies and the corresponding application scenarios. Results of static and kinematic experiments conducted in indoor and outdoor environment show that positioning accuracies at the meter level can be achieved using RFID based integrated systems. The algorithms developed can be readily deployed in portable multi-sensor positioning systems for LBS.

Map-Based LBSs for Hiking

In a map-based location-based service, a map on a mobile device is used to visualize and communicate spatial information to the user. The objective of this chapter is to provide a review of map-based LBSs that are especially directed to outdoor activities such as hiking. Hikers as users have special information needs and requirements, which, as in any development, should provide the starting point and the goal for the LBS. The authors list the requirements and solutions such a service must address and discuss the implications of using maps for the application development. They discuss the essential components, functionality, and architectural solutions of an LBS for hiking, and describe the functionality needed for wayfinding and navigation support. Finally, the authors portray the emerging trends, such as ubiquity, adaptivity, and personal cloud solutions, that will motivate future research.

Hybrid Positioning with Smart Phones

Ubiquitous positioning is intended for providing smooth and seamless positioning solution across indoor and outdoor environments, and it is usually achieved through a hybrid positioning scheme that integrates multiple positioning technologies. This chapter explores various aspects of hybrid positioning with smartphones. First, it presents a hybrid positioning solution based on hidden Markov models (HMM). The hybrid positioning solution utilizes multiple location sensors and signals of opportunity that are available in smartphones, and integrates multiple relative and absolute positioning technologies to achieve ubiquitous positioning. The hybrid positioning solution provides a significant improvement over individual positioning technologies in positioning availability, accuracy, and reliability. Then, this chapter generalizes the concept and the methodology of hybrid positioning. Different relative and absolute positioning technologies are comprehensively reviewed based on their respective operation principles. These positioning technologies have the potential to be integrated in a specific hybrid positioning scheme. This chapter is concluded with some considerations for designing a deliberate hybrid positioning solution followed by a short summary of the whole chapter.

Assisted GNSS in Smart Phones

It is difficult to acquire GNSS (Global Satellite Navigation System) signals in challenging environments such as urban canyons and indoors without any assistance information. The time-to-first-fix process is extremely long for positioning in such environments, especially for the case when the user is moving. In order to speed up the time-to-first-fix process, the receiver needs assistance information from a server for quickly acquiring and tracking the GNSS signals. This chapter introduces the assisted GNSS solution. It covers the topics of acquisition assistance, sensitivity assistance, and implementation for smart phones. The objective of the chapter is to provide the readers with a general overview of the Assisted-GNSS (A-GNSS) solution from the aspects of how the A-GNSS solution speeds up the acquisition process and improves the tracking sensitivity, what kinds of assistance data are needed, and how the assistance data is delivered to the mobile users. The chapter includes sections for an introduction to the topic, acquisition assistance, sensitivity assistance, A-GNSS implementation for smart phones, future directions, and brief conclusions.

Visual Positioning in a Smartphone

Numerous techniques for obtaining motion and location related information are needed for obtaining seamless positioning capability with smartphones. High-quality cameras are nowadays widely available in portable devices and can provide necessary redundant data about the user’s surroundings in addition to the other sensors usable for positioning purposes. In this chapter, after introducing methods of image processing related to feature extraction, applicable methods for visual positioning are discussed with state-of-the-art examples. Regardless whether the visual-based positioning is based on reference images in a database or using information obtained from consecutive images, the first steps of pre-processing are similar to obtain noiseless images for accurate calculations and to retrieve the required camera parameters. Smartphones have limited computational resources and that restricts the methods available for image processing. To carry out the visual positioning function, features in images are either matched to corresponding features in consecutive images, or to a database. Obtaining the location can be performed with matching the query image to a database of reference images equipped with location information. Alternatively, the attitude and position change can be resolved from consecutive images to provide localization augmentation that may be fused with other sensor information. When smartphones are concerned, the restricted resources however bring about challenges that are the focus in this chapter.

WLAN and Bluetooth Positioning in Smart Phones

Although the short range radio frequency technologies such as WLAN (Wireless Local Area Network) and Bluetooth were originally designed for the purpose of wireless communication, they have been widely adopted as common signals of opportunity for positioning in smart phones for both indoors and outdoors. The cell identifier and radio signal strength are the most common observables used for positioning. The applicable position methods include Cell-ID, fingerprinting, and trilateration. Fingerprinting is the most common approach, which can provide a positioning accuracy of even 2-5 meters indoors using either the pattern recognition algorithm or the probabilistic algorithm; however, the obtainable accuracy depends on the positioning environment. The objective of this chapter is to present the WLAN and Bluetooth positioning methodologies and explain the related positioning algorithms. The chapter covers an introduction of the topic, descriptions of the observables, the positioning algorithms, and the implementation issues of the positioning solutions. The chapter is concluded with a short section of future research directions followed by a brief conclusion.

Introduction to Smart Phone Positioning

Ubiquitous positioning is now becoming a key technology for Location-Based Service (LBS) in smart phones. For outdoor environments, it is not a challenging task to locate mobile users using the Global Navigation Satellite System (GNSS). However, it is still difficult to locate the smart phones with a sufficient accuracy in the GNSS degraded or denied environments, such as urban canyons and indoors. Additional sensors and signals of opportunity are needed to augment the GNSS solution for such environments. The sensors include accelerometers, gyroscope, digital compass, while the signals of opportunity include the RF (Radio Frequency) signals from WLAN (Wireless Local Area Network) and Bluetooth networks. These sensors and signals of opportunity are typically available in smart phones even though most of them are not originally intended for positioning purpose. There are four positioning solutions that can be applied to smart phones: the GNSS-based positioning solution, the RAN (Radio Access Network)-based positioning solution, the positioning solution based on the signals of opportunity from short range RF technologies, and the hybrid positioning solution that utilizes multiple sensors and multiple signals of opportunity. The objective of this chapter is to give an introduction to these positioning solutions, including the positioning infrastructures, the sensors, the characteristics of the signals, and the fundamental positioning algorithms.

Mobile Geographic Information Systems

This chapter introduces the concept of Mobile Geographical Information Systems (Mobile GIS) as an evolution of conventional GIS to being available on wireless mobile devices such as smart phones. The evolution of the technology and its applications are charted in this chapter. The main elements of Mobile GIS are then discussed. This focuses on: GIS servers; wireless mobile telecommunication networks; wireless mobile devices; location-awareness technology; and gateway services. This is followed by a discussion of the main features in terms of the services and usage of Mobile GIS: mobility; real-time connectivity; location-awareness; broadened usage. Mobile Geographical Information Systems are an important facilitating technology for Location-Based Services (LBS). A range of applications of Mobile GIS for smart phones are described. The chapter closes with a discussion of the prospects and challenges for Mobile GIS. Challenges derive from four broad areas: limitations that derive from the technologies being used; areas of GIScience that still need to be adequately researched; users; and business models for a sustainable presence.