Motion Compatibility for Indoor Localization

Indoor localization -- a device's ability to determine its location within an extended indoor environment -- is a fundamental enabling capability for mobile context-aware applications. Many proposed applications assume localization information from GPS, or from WiFi access points. However, GPS fails indoors and in urban canyons, and current WiFi-based methods require an expensive, and manually intensive, mapping, calibration, and configuration process performed by skilled technicians to bring the system online for end users. We describe a method that estimates indoor location with respect to a prior map consisting of a set of 2D floorplans linked through horizontal and vertical adjacencies. Our main contribution is the notion of "path compatibility," in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for agreement with the prior map. Path compatibility is encoded in an HMM-based matching model, from which the method recovers the user s location trajectory from the low-level motion estimates. To recognize user motions, we present a motion labeling algorithm, extracting fine-grained user motions from sensor data of handheld mobile devices. We propose "feature templates," which allows the motion classifier to learn the optimal window size for a specific combination of a motion and a sensor feature function. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our motion labeling algorithm classifies user motions with 94.5% accuracy, and our trajectory matching algorithm can recover the user's location to within 5 meters on average after one minute of movements from an unknown starting location. Prior information, such as a known starting floor, further decreases the time required to obtain precise location estimate

Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios

In the last decades, the rise of life expectancy has accelerated the demand for new technological solutions to provide a longer life with improved quality. One of the major areas of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose, indoor positioning systems are valuable tools and can be classified depending on the need of a supporting infrastructure. Infrastructure-based systems require the investment on expensive equipment and existing infrastructure-free systems, although rely on the pervasively available characteristics of the buildings, present some limitations regarding the extensive process of acquiring and maintaining fingerprints, the maps that store the environmental characteristics to be used in the localisation phase. These problems hinder indoor positioning systems to be deployed in most scenarios. To overcome these limitations, an algorithm for the automatic construction of indoor floor plans and environmental fingerprints is proposed. With the use of crowdsourcing techniques, where the extensiveness of a task is reduced with the help of a large undefined group of users, the algorithm relies on the combination ofmultiple sources of information, collected in a non-annotated way by common smartphones. The crowdsourced data is composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into smaller groups, each corresponding to specific areas of the building. Distance metrics applied to magnetic field signals are used to identify geomagnetic similarities between different users’ trajectories. The building’s floor plan is then automatically created, which results in fingerprints labelled with physical locations. Experimental results show that the proposed algorithm achieved comparable floor plan and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free systems. With these results, this solution can be applied in any fingerprinting-based indoor positioning system

Map matching by using inertial sensors: literature review

This literature review aims to clarify what is known about map matching by using inertial sensors and what are the requirements for map matching, inertial sensors, placement and possible complementary position technology. The target is to develop a wearable location system that can position itself within a complex construction environment automatically with the aid of an accurate building model. The wearable location system should work on a tablet computer which is running an augmented reality (AR) solution and is capable of track and visualize 3D-CAD models in real environment. The wearable location system is needed to support the system in initialization of the accurate camera pose calculation and automatically finding the right location in the 3D-CAD model. One type of sensor which does seem applicable to people tracking is inertial measurement unit (IMU). The IMU sensors in aerospace applications, based on laser based gyroscopes, are big but provide a very accurate position estimation with a limited drift. Small and light units such as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very popular, but they have a significant bias and therefore suffer from large drifts and require method for calibration like map matching. The system requires very little fixed infrastructure, the monetary cost is proportional to the number of users, rather than to the coverage area as is the case for traditional absolute indoor location systems.Siirretty Doriast

Smartphone application for accessible navigation

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2018.The main aim of this study is to investigate how the modern smartphone technology can assist people with visual impairments in indoor navigation tasks. We use the free and open indoor navigation service Anyplace, to design an indoor guidance system that is accessible, inexpensive, simple and user-friendly to different user groups disregarding their disabilities. The Android application that Anyplace offers, was extended and modified to serve also the needs of visually impaired users. The presented system works well with the assistive applications that Android platform offers and provides various ways for interaction between the user and the system. The system is communicating with Anyplace server to inform the user about the information of the surrounding environment and guide him/her to the desired place in the building with accessible messages. The application can process, specific pre-defined user commands and location information from existing QR labels in the building. This thesis is focusing on assisting the impaired users on indoor navigation tasks, but not on replacing the assistive means that the visually impaired user is already using. (e.g. long cane, guide dog) Experimental results show the ability of the system to effectively communicate with the user and assist him/her in way-finding tasks in the building of the University of Macedonia

Wi-Fi Measurement Campaign for Indoor Localization

Most of the day to day people activities are carried out inside buildings. Many sectors such as, medicine, industry, academia or even security systems require indoor positioning services. As a consequence, it is essential to develop a reliable and accurate indoor positioning system (IPS). Since global navigation satellite systems (GNSSs) are not suitable for indoor localization, several IPSs have emerged. However, each indoor positioning technology has its advantages and disadvantages. Hence, there is not an IPS system with the best performance for every situation. The IPS databases based on the Wi-Fi infrastructure installed in two buildings of the Tampere University of Technology required an update. Therefore, the scope of this thesis has been to update and moreover, optimize the IPS fingerprint databases of these two buildings. The results have been presented and analyzed with the expectance that they will be useful for similar or wider projects. Multiple IPSs are explained, as it is convenient to understand the advantages and the weaknesses of each technology. The technology which provides the positioning services is the fingerprint Wi-Fi received signal strength (RSS). In that way, a measurement database is built. The database is used to simulate the IPS, which is implemented through the Bayesian estimation algorithm and the k-nearest neighbors technique. Successively, the parameters of the algorithm are optimized. The analysis of the results showed that for the lowest values of the parameters, the performance of the system improves with respect to higher values of the parameters. The best performance of the Wi-Fi based IPS results in a floor detection probability nearby 99% and an average distance error below 3 m. However, negative effects, such as the ones produced by outlier measurements, must be taken into account. Some weaknesses of the Wi-Fi based IPS, such as the challenges associated to the training phase, open a path of research that might enhance the system performance