Computing and Information Systems - Theses
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ItemIndoor localization supported by landmark graph and locomotion activity recognition( 2018)Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Although a number of indoor localization systems have been proposed in recent years, they have different limitations in terms of accuracy, cost, coverage, complexity, and applicability. In order to achieve a higher accuracy with relatively low cost, hybrid methods combining multiple positioning techniques have been used. However, hybrid methods usually require an infrastructure of beacons or transmitters, which may not be available in many environments or it may be available at a high cost. Spatial knowledge is available in many scenarios, and can be used to assist localization without additional cost. Landmarks are one of the spatial constraints useful for indoor localization. Indoor localization systems that use landmarks have been proposed in the literature, but they are usually applied for tracking robots by using laser scanners or/and cameras. The systems using these devices are economically or/and computationally expensive, and hence are not suitable for indoor pedestrian localization. Although landmarks based on the built-in smartphone sensors are also used in some indoor localization systems, the performance of these systems relies highly on the completeness of landmarks. A mismatch of landmarks may cause a large localization error and even lead to the failure of localization. The advent of sensor-equipped smart devices has enabled a variety of activity recognition and inference, including locomotion (e.g., walking, running, standing). The sensors built in the smart devices can capture the intensity and duration of activity, and even are able to sense the activity context. Such information can be used to enhance the localization accuracy or reduce the energy consumption and deployment cost while maintaining the accuracy. For example, the knowledge of locomotion activities can be used to optimize the step length estimation of people, which will contribute to the improvement of localization accuracy. However, it is challenging to precisely recognize activities related to indoor localization with smartphones. The hypothesis of this research is that accurate and reliable indoor localization can be achieved by fusing smartphone sensor data with locomotion activities and a landmark graph. This hypothesis is tested using the novel algorithms proposed and developed in this research. The proposed framework consists of four main phases, namely recognizing locomotion activities related to indoor localization from sensor data, improving the accuracy of step counting and step length estimation for pedestrian dead reckoning method, developing a landmark graph-based indoor localization method, and implementing quick WiFi fingerprint collection. The main contributions of this research are as follows. First, a novel method is proposed for locomotion activity recognition by automatically learning useful features from sensor data using a deep learning model. Second, robust and accurate algorithms are proposed for step counting and step length estimation to improve the performance of pedestrian dead reckoning, which will be fused with spatial information. Third, the concept of sensory landmarks and the landmark graph is proposed, and a landmark graph-based method is developed for indoor localization. Fourth, a practical, fast, and reliable fingerprint collection method is designed, which uses the landmark graph-based localization method for automatically estimating the location of reference points used to associate the collected fingerprints.