Abstract
There is an inherent problem of error accumulation in Pedestrian Dead Reckoning (PDR). In this chapter, we introduce a PDR error compensation scheme based on the assumption that can obtain sparse locations. Sparse locations are discontinuous locations obtained by using an absolute localization method or passage detection devices (ex. RFID tag, BLE beacon, Spinning Magnet Marker). Our proposal scheme focuses on being able to install anywhere in the indoor environment. In our scheme, we define error models that represent errors in PDR, including moving distance error and orientation change error. We apply the error models to counteract the error that occurs in PDR estimation. Moreover, the error models are tuned each time when a sparse location is measured. As a result, the proposed scheme improves the position error rate by approximately 10% and the route distance error rate by approximately 7%. In addition, we discuss the effectiveness of our scheme by each test route for future consideration.
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Abe M, Kaji K, Hiroi K, Kawaguchi N (2016) PIEM: path independent evaluation metric for relative localization. In: 2016 international conference on indoor positioning and indoor navigation (IPIN), pp 1–8
Ban R, Kaji K, Hiroi K, Kawaguchi N (2015) Indoor positioning method integrating pedestrian dead reckoning with magnetic field and WiFi fingerprints. In: 8th international conference on mobile computing and ubiquitous networking (ICMU). IEEE, pp 167–172
Chang Q, Van de Velde S, Wang W, Li Q, Hou H, Heidi S (2015) Wi-Fi fingerprint positioning updated by pedestrian dead reckoning for mobile phone indoor localization, pp 729–739
Ciabattoni L, Foresi G, Monteriù A, Pepa L, Pagnotta DP, Spalazzi L, Verdini F (2017) Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons. J Ambient Intell Hum Comput
Faragher R, Harle R (2015) Location fingerprinting with bluetooth low energy beacons. IEEE J Sel Areas Commun 33(11):2418–2428
Farshad A, Li J, Marina MK, Garcia FJ (2013) A microscopic look at WiFi fingerprinting for indoor mobile phone localization in diverse environments. In: International conference on indoor positioning and indoor navigation, vol 28, p 31
Ferris B, Fox D, Lawrence N (2007) WiFi-SLAM using Gaussian process latent variable models. In: 20th international joint conference on artificial intelligence, IJCAI’07. Morgan Kaufmann Publishers Inc., pp 2480–2485
Geospatial EXPO 2016. http://g-expo.jp/2016/. Accessed 01 Feb 2019
Kaji K, Watanabe H, Ban R, Kawaguchi N (2013) HASC-IPSC: indoor pedestrian sensing corpus with a balance of gender and age for indoor positioning and floor-plan generation researches. In: Proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication. ACM, pp 605–610
Kang W, Han Y (2015) SmartPDR: smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sens J 15(5):2906–2916
Krach B, Roberston P (2008) Cascaded estimation architecture for integration of foot-mounted inertial sensors. In: 2008 IEEE/ION position, location and navigation symposium. IEEE, pp 112–119
Li J, Wang Q, Liu X, Cao S, Liu F (2014) A pedestrian dead reckoning system integrating low-cost MEMS inertial sensors and GPS receiver. J Eng Sci Technol Rev 7(2)
Miyake T, Arai I (2013) An adaptive step length reasoning for time periods and members of a user’s party [in Japanese]. Spec Interes Group Tech Rep IPSJ 32:1–7
Nozaki J, Hiroi K, Kaji K, Kawaguchi N (2017) Compensation scheme for PDR using sparse location and error model. In: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers, UbiComp ’17, New York, NY, USA. ACM, pp 587–596
Oberli C, Torres-Torriti M, Landau D (2010) Performance evaluation of UHF RFID technologies for real-time passenger recognition in intelligent public transportation systems. IEEE Trans Intell Transp Syst 11(3):748–753
Shin B, Lee JH, Lee H, Kim E, Kim J, Lee S, Cho Y, Park S, Lee T (2012) Indoor 3D pedestrian tracking algorithm based on PDR using smarthphone. In: 12th international conference on control, automation and systems, pp 1442–1445
Takeshima C, Kaji K, Hiroi K, Kawaguchi N, Kamiyama T, Ohta K, Inamura H (2015) A pedestrian passage detection method by using spinning magnets on corridors. In: Adjunct proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2015 ACM international symposium on wearable computers. ACM, pp 411–414
Zheng L, Zhou W, Tang W, Zheng X, Peng A, Zheng H (2016) A 3D indoor positioning system based on low-cost MEMS sensors. Simul Model Pract Theory 65:45–56
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Part of this research was supported by the Executive Committee of Geospatial EXPO 2016 indoor localization x IoT demonstration experiment, JSPS KAKENHI Grant Number JP 17H01762.
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Nozaki, J., Hiroi, K., Kaji, K., Kawaguchi, N. (2019). Compensation Scheme for PDR Using Component-Wise Error Models. In: Kawaguchi, N., Nishio, N., Roggen, D., Inoue, S., Pirttikangas, S., Van Laerhoven, K. (eds) Human Activity Sensing. Springer Series in Adaptive Environments. Springer, Cham. https://doi.org/10.1007/978-3-030-13001-5_3
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DOI: https://doi.org/10.1007/978-3-030-13001-5_3
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