Indoor Localization Based on Magnetic Anomalies and Pedestrian Dead Reckoning

J. Ma, J. Qian, P. Li, R. Ying, P. Liu

Abstract:	Indoor localization is regularly related to beacons or war-driving. However, indoor geo-magnetism based indoor localization is one of the few which is beacon-free. Evidence[1] shows that indoor geo-magnetism which has been distorted by both the steel and concrete skeletons of modern building as well as man-made sources such as electric power systems and electric and electronic devices remains static and extremely low-frequency for several months. These kind of stable indoor magnetic anomalies in other words magnetic landmarks have been adopted to establish indoor localization systems [1] [2]. However, those systems depend on the prior knowledge of the magnetic map data which is collected previously to work. The magnetic map which frequently involves a large amount of raw magnetic data requires great effort to establish. In this paper, pedestrian dead reckoning (PDR) is integrated with indoor magnetic anomalies to provide indoor localization and mapping using foot-mount IMUs and magnetometers in an unsupervised way. We apply indoor magnetic measurements from magnetometers to discover the landmark rather than to store every magnetic measurement collected. Magnetic landmarks’ pattern is discovered through the travel of the floor plan. At the meantime, landmark together with the trajectory provided by PDR determine the location of landmarks as well as the location of the pedestrian. Our system discovers magnetic landmark adopting north-east-earth measurement of magnetic flied through the travel of the floor plan, so neither the knowledge of the magnetic landmark’s location nor the floor plan is needed. Database which consists of large amounts of raw magnetic measurements is also avoided. Magnetometers have been applied in robot localization. In those applications, the movement of a robot is mainly smooth and restrict to 2D which enables the implement of north-east-earth measurement of geomagnetic field. Due to the fact that the motion of a human is much more complicated, modulus of 3-axis magnetic measurement is commonly applied. But in our system, we still have the access to the north-east-earth measurement of magnetic flied since we have acquired the attitude of the sensor through AHRS algorithm and rotate the sensor-frame measurements into the north-east-earth frame coordinate. North-east-earth measurement of the magnetic field can distinguish magnetic landmark more accurate than using only the modulus. Though there are magnetic landmarks scattered in the indoor environment, we have no idea where they are or what they look likes. In order to discover magnetic landmarks in an unsupervised way, we first implement a magnetic landmark discover algorithm holding the assumption that the floor plan is available (The assumption will be relaxed later). The magnetic landmark search algorithm is closely related to the idea of motif search which is well-known in the data-mining and knowledge-discover community which refers to a previously unknown pattern that appears frequently in a time-series. Moreover, we expect the motifs to be densely clustered in spatial rather than scattered all over the floor plan. As the speed of the feet is varying for the interval of the pace, each time a pedestrian walk through a magnetic landmark, the magnetic anomaly pattern could be slightly different. To correctly identify a magnetic landmark, we apply the dynamic time warping and hidden Markov Models which are appreciated in the voice recognition applications where the same word spoken could be different in length, tone and volume. A naive motif search algorithm could be quadratic time which is infeasible even for medium size database. However several methods have been proposed to accelerate the search. In our system, trajectory of the pedestrian is provided by pedestrian dead reckoning. PDR based on IMU measurements benefits from zero velocity updates (ZUPT) when IMUs are installed on the pedestrian’s foot to acquire reasonable accuracy using cheap but noisy sensors. However, PDR suffers from cumulative error, so it needs calibrations from landmarks regularly. We adopt the idea of simultaneous-localization-and-mapping (SLAM) which combines PDR trajectory and the magnetic anomaly landmarks to locate the pedestrian as well as the landmarks at the same time. SLAM is feasible if the trajectory of the pedestrian and the magnetic landmark measurement is available. Though the trajectory provided by PDR is available all the time, magnetic landmark discover algorithm will need repeated cover of the floor plan to find reasonable and stable landmarks. To conquer this, two other landmarks can help. First is the IMU-based landmark such as turn of the Pedestrian which refers to the corner of the floor plan. Second is the empirical magnetic landmark which is acquired previously. The proposed system contributes in creation of a method to discover magnetic anomalies pattern and integration magnetic landmark and PDR to acquire reliable localization accuracy. Details of the magnetic anomaly landmark discover as well as mapping and localization algorithm will be discussed in this paper. Performance measurements in real-world environments are also presented. Reference: [1] J Haverinen, A Kemppainen, “Global indoor self-localization based on the ambient magnetic field”, Robotics and Autonomous Systems, 2009 – Elsevier [2] J. Chung, M. Donahoe, C. Schmandt, I. J. Kim, P. Razavai and M. Wiseman, "Indoor location sensing using geo-magnetism," in Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, 2011, pp. 141-154.
Published in:	Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013) September 16 - 20, 2013 Nashville Convention Center, Nashville, Tennessee Nashville, TN
Pages:	1033 - 1038
Cite this article:	Ma, J., Qian, J., Li, P., Ying, R., Liu, P., "Indoor Localization Based on Magnetic Anomalies and Pedestrian Dead Reckoning," Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September 2013, pp. 1033-1038.
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