Indoor Human Navigation Systems: A Survey <s> Pattern Recognition <s> The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. <s> BIB001 </s> Indoor Human Navigation Systems: A Survey <s> Pattern Recognition <s> This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field. To make sense of this domain, we have developed a taxonomy to help developers of location-aware applications better evaluate their options when choosing a location-sensing system. The taxonomy may also aid researchers in identifying opportunities for new location-sensing techniques. <s> BIB002 </s> Indoor Human Navigation Systems: A Survey <s> Pattern Recognition <s> While wireless sensor networks offer new capabilities, there are a number of issues that hinder their deployment in practice. We argue that robotics can solve or greatly reduce the impact of many of these issues. Our hypothesis has been tested in the context of an autonomous system to care for houseplants that we have deployed in our office environment. This paper describes what we believe is needed to make sensor networks practical, the role robots can play in accomplishing this, and the results we have obtained in developing our application. <s> BIB003 </s> Indoor Human Navigation Systems: A Survey <s> Pattern Recognition <s> In this paper we present a new system that assists blind users in orienting themselves in indoor environments. We developed a sensor module that can be handled like a flashlight by a blind user and can be used for searching tasks within the three-dimensional environment. By pressing keys, inquiries concerning object characteristics, position, orientation and navigation can be sent to a connected portable computer, or to a federation of data servers providing models of the environment. Finally these inquiries are acoustically answered over a text-to-speech engine. <s> BIB004 </s> Indoor Human Navigation Systems: A Survey <s> Pattern Recognition <s> In the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environements) at our University we are working on the improvement of navigation services for pedestrians. Thereby we are mainly focusing on the information aspect of location-based services, i.e., on the user’s task at hand and the support of the user’s decisions by information provided by such a service. Specifications will allow us to select appropriate sensor data and to integrate data when and where needed, to propose context-dependent routes fitting to partly conflicting interests and goals as well as to select appropriate communication methods in terms of supporting the user guidance by various multimedia cartography forms. These taks are addressed in the project in three different work packages, i.e., the first on “Integrated positioning”, the second on “Pedestrian route modeling” and the third on “Multimedia route communication”. In this paper we will concentrate on the research work and findings in the first work package. For continuous positioning of a pedestrian suitable location technologies include GNSS and indoor location techniques, cellular phone positioning, dead reckoning sensors (e.g. magnetic compass, gyro and accelerometers) for measurement of heading and travelled distance as well as barometric pressure sensors for height determination. The integration of these sensors in a modern multi-sensor system can be performed using an adapted Kalman filter. To test and to demonstrate our approach, we take a use case scenario into account, i.e., the guidance of visitors to departments of the Vienna University of Technology. The results of simulation studies and practical tests could confirm that such a service can achieve a high level of performance for the guidance of a pedestrian in urban areas and mixed indoor and outdoor environments. <s> BIB005 </s> Indoor Human Navigation Systems: A Survey <s> Pattern Recognition <s> Many navigation systems for visually impaired people have been developed, but few can provide dynamic interactions and adaptability to changes. The aim of this working life project is to create an innovative navigation service solution that will improve the quality of life of the visually impaired. For example, a university campus might be such a large and rambling place that visually impaired people can not always find the services or rooms they want to. Therefore, we are devising new service solutions that exploit the technical features of modern WLAN systems in an innovative way. This paper describes the LaureaPOP system that focuses on the indoor navigation service design in a wireless local area network environment. <s> BIB006 </s> Indoor Human Navigation Systems: A Survey <s> Pattern Recognition <s> We describe a body-worn sensor suite, environment representation, set of algorithms, and graphical-aural interface designed to provide human-centered guidance to a person moving through a complex space. The central idea underlying our approach is to model the environment as a graph of visually distinctive places (graph nodes) connected by path segments (graph edges). During exploration, our algorithm processes multiple video-rate inputs to identify visual features and construct the “place graph” representation of the traversed space. The system then provides visual and/or spoken guidance in user-centered terms to lead the user along existing or newly-synthesized paths. Our approach is novel in several respects: it requires no precise calibration of the cameras or multi-camera rig used; it generalizes to any number of cameras with any placement on the body; it learns the correlation between user motion and evolution of image features; it constructs the place graph automatically; and it provides only coarse (rather than precise metrical) guidance to the user. We present an experimental study of our methods applied to walking routes through both indoor and outdoor environments, and show that the system provides accurate localization and effective navigation guidance. <s> BIB007
Pattern recognition based localization methods use data from one or more sensors carried or worn by the user and compare this perceived data with set of prior collected raw sensor data that has been coupled with an environment map. This map of sensor data can be created by sampling at different locations or by creating it manually. Most human navigation systems use a combination of different sensing techniques: • Computer vision based localization techniques  BIB004  BIB005 require the user to either carry a camera or use a camera embedded in a hand-held device, such as a cell phone. While users navigate in an environment, a camera captures images of the environment, and then by matching the images against a database of images with known location, users' position and orientation can be determined. The camera captures images while the user navigates. Using image matching the users' position and orientation can be determined BIB007  . A disadvantage of this technique is the high storage capacity required for storing the images that are coupled with the environment map. Significant computing power may be required to perform the image matching BIB002 , which may be challenging to implement on a handheld device. Users are often required to carry supporting computing equipment   , which may impede their mobility. • Signal distribution or fingerprinting localization techniques compare the unique signal data from one or more external sources sensed at a particular location with a map of prerecorded data. This technique requires a training phase, which the received signal strength at different locations are acquired and then stored in a database to create a map. In the next phase, when the user is navigating, the received signal strength or its distribution over time is measured and compared with the map to find the closest match. The signal strength from WLAN (Wireless Local Area Networks) access points BIB006 BIB005 is an example of signal distribution localization. An advantage of WLAN signal localization is the relatively small number of base stations that are required for localizing the user BIB001 . Due to the increased prevalence of wireless networks in indoor environments, often no investment in infrastructure is required as existing base stations can be used BIB002 . Other signal distribution localization techniques typically rely on a combination of low cost sensors such as an accelerometer, magnetometer (measuring the strength and direction of a magnetic field), temperature, and light sensors  . Creating a map for a multitude of sensors is often time consuming and furthermore, the map may not be reliable as some signals such as temperature and light sensors may be subject to daily or seasonal fluctuations BIB003 . Table 1 shows an overview of the different localization techniques and the different technologies used within each technique.