Abstract
This paper presents the concept of using embedded MEMS sensors position objects especially when the GPS signal is weak, e.g. in underground car parks, tunnels. Such an approach is important for controlling indoor objects or autonomous vehicles. The signals are acquired by a Raspberry Pi platform with external sensors such as an accelerometer, gyroscope and magnetometer. A self-propelled vehicle was used and several exemplary paths were designed for acquiring signals. It was proven that appropriate signal filtering allows a position to be determined with a small error at a constant velocity condition. Comparing filters such as the moving average, median, Savitzky-Golay and Hampel filters were investigated. Moreover, the system offers a high degree of accuracy in a short time for indoor hybrid positioning systems that also have video processing capabilities. The cyber-physical system can also be used with the existing infrastructure in a building, such as Wi-Fi access points and video cameras.
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Acknowledgements
This work was supported by the European Union from the FP7-PEOPLE-2013-IAPP AutoUniMo project “Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model” (grant agreement no: 612207) and research work financed from funds for science in years 2016–2017 allocated to an international co-financed project (grant agreement no: 3491/7.PR/15/2016/2) and supported by Polish Ministry of Science and Higher Education with subsidy for maintaining research potential.
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Grzechca, D., Tokarz, K., Paszek, K., Poloczek, D. (2017). Using MEMS Sensors to Enhance Positioning When the GPS Signal Disappears. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_25
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