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.
IEEE Journal on Selected Areas in Communications 01/2015; DOI:10.1109/JSAC.2015.2430518 · 4.14 Impact Factor
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ABSTRACT: This paper proposes a foot-mounted zero velocity update (ZVU) aided inertial measurement unit (IMU) filtering algorithm for pedestrian tracking in indoor environment. The algorithm outputs are the foot kinematic parameters that include foot orientation, position, velocity, acceleration, and gait phase. The foot motion filtering algorithm incorporates methods for orientation estimation, gait detection, and position estimation. A novel complementary filter is introduced to better preprocess the sensor data from a foot-mounted IMU containing triaxial angular rate sensors, accelerometers, and magnetometers and to estimate the foot orientation without resorting to global positioning system data. A gait detection is accomplished using a simple states detector that transitions between states based on acceleration and angular rate measurements. Once foot orientation is computed, position estimates are obtained using integrating acceleration and velocity data, which has been corrected at step stance phase for drift using an implemented ZVU algorithm, leading to a position accuracy improvement. We show our findings experimentally by using of a commercial IMU during regular human walking trials in a typical public building. Experiment results show that the positioning approach achieves approximately a position accuracy around 0.4% and improves the performance regarding recent works of literature.IEEE Transactions on Instrumentation and Measurement 01/2015; 64(1):221-229. DOI:10.1109/TIM.2014.2335912 · 1.71 Impact Factor
conielecomp, Puebla; 02/2015