Global indoor self-localization based on the ambient magnetic field
ABSTRACT There is evidence that animals utilize local anomalities of Earth’s magnetic field not just for orientation detection but also for true navigation, i.e., some animals are not only able to detect the direction of Earth’s magnetic field (compass heading), they are able to derive positional information from local cues arising from the local anomalities of Earth’s magnetic field. Similarly to Earth’s non-constant magnetic field, the magnetic field inside buildings can be highly non-uniform. The magnetic field fluctuations inside buildings arise from both natural and man-made sources, such as steel and reinforced concrete structures, electric power systems, electric and electronic appliances, and industrial devices. Assuming that the anomalities of the magnetic field inside a building are nearly static and they have sufficient local variability, the anomalies provide a unique magnetic fingerprint that can be utilized in global self-localization. Based on the evidence presented in this article it can be argued that this hypothesis is valid. In this article, a Monte Carlo Localization (MCL) technique based on the above hypothesis is proposed. The feasibility of the technique is demonstrated by presenting a series of global self-localization experiments conducted in four arbitrarily selected buildings, including a hospital. The experiment setup consists of a mobile robot instrumented with a 3-axis magnetometer and a computer. In addition to global robot self-localization experiments, successful person self-localization experiments were also conducted by using a wireless, wearable magnetometer. The reported experiments suggest that the ambient magnetic field may remain sufficiently stable for longer periods of time giving support for self-localization techniques utilizing the local deviations of the magnetic field.
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ABSTRACT: Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.IEEE Transactions on Mobile Computing 11/2014; · 2.40 Impact Factor
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ABSTRACT: The need for accurate indoor localization increases as we get used to accessible outdoor localization, and the number of applications depending on localization grows. Indoor localization is challenging because of frequent line of sight obstructions and dynamic changes in the environment. Magnetometers can be found in many modern electronic devices and provide a simple way to measure the geomagnetic field intensity. Due to distortions in this magnetic field, these measurements often provide enough information to enable identification of a location using pattern matching. We show the feasibility of using these magnetic field intensity measurements in localization and SLAM applications. Our SLAM system of choice is the biologically inspired RatSLAM, as it allows pattern matching as scene recognition. We demonstrate a number of experiments in various environments, including a suburban house and a university lab. We conclude that geomagnetic localization and SLAM are feasible in environments with many distortions in the magnetic field. Such locations are easier to identify than locations with little distortions, which will have the same pattern of magnetic field over larger areas.International Journal On Advances in Systems and Measurements. 01/2014; 7(1&2):44-56.
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ABSTRACT: Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements. By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis. Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.ACM Transactions on Intelligent Systems and Technology (TIST). 09/2013; 4(4).