Abstract
With the increasing demand for indoor navigation applications, indoor navigation has become a research hotspot in many technical fields. High-precision sensors are expensive, and they are often used in industrial and aerospace applications. Low-cost sensors can cause severe drift and noise. This paper uses a simple, low-cost Inertial measurement unit (IMU) and effectively evaluates the precise position of pedestrians. This paper designs a pedestrian position estimation based on low-cost inertial sensors. To solve the problem of inertial sensing noise and accumulated errors during the movement, based on the EKF algorithm, a multi-condition threshold detection method is proposed, and then a zero-speed update, an angular speed update, and a yaw angle update are designed. In the measurement state, the proposed measurement error vector effectively eliminates the cumulative error during walking. Finally, using this algorithm to perform straight and rectangular walking routes, corresponding experiments were performed on the effectiveness of the method used. The proposed method for estimating the inertial navigation of pedestrians has an error within 3.8%. This basic research is of great significance in rehabilitation training and somatosensory play.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cai, C.l., Liu, Y., Liu, Y.W.: Status quo and trend of inertial integrated navigation system based on mems. J. Chin. Inertial Technol. 5, 562–567 (2009)
Diaz, E.M., Gonzalez, A.L.M., de Ponte Müller, F.: Standalone inertial pocket navigation system. In: 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, pp. 241–251. IEEE (2014)
Du, Y., Arslan, T.: Magnetic field indoor positioning system based on automatic spatial-segmentation strategy. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8. IEEE (2017)
Ebner, M., Zell, A.: Centering behavior with a mobile robot using monocular foveated vision. Robot. Auton. Syst. 32(4), 207–218 (2000)
Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graphics Appl. 25(6), 38–46 (2005)
House, S., Connell, S., Milligan, I., Austin, D., Hayes, T.L., Chiang, P.: Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7598–7601. IEEE (2011)
Husen, M.N., Lee, S.: Indoor location sensing with invariant WI-FI received signal strength fingerprinting. Sensors 16(11), 1898 (2016)
Jiménez, A.R., Seco, F., Prieto, J.C., Guevara, J.: Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU. In: 2010 7th Workshop on Positioning, Navigation and Communication, pp. 135–143. IEEE (2010)
Kim, Y.H., Song, U.K., Kim, B.K.: Development of an accurate low-cost ultrasonic localization system for autonomous mobile robots in indoor environments. J. Meas. Sci. Instrum. 1, 16 (2010)
Ligorio, G., Sabatini, A.M.: A novel Kalman filter for human motion tracking with an inertial-based dynamic inclinometer. IEEE Trans. Biomed. Eng. 62(8), 2033–2043 (2015)
Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), pp. 407–415. IEEE (2003)
Özsoy, K., Bozkurt, A., Tekin, İ.: 2D indoor positioning system using GPS signals. In: 2010 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–6. IEEE (2010)
Peterson, B., Bruckner, D., Heye, S.: Measuring GPS signals indoors. In: 9th world congress of the International Association of Institutes of Navigation, Amsterdam, 18–21 November 1997 (1997)
Song, J.W., Park, C.G.: Enhanced pedestrian navigation based on course angle error estimation using cascaded Kalman filters. Sensors 18(4), 1281 (2018)
Tsai, F., Chiou, Y.S., Chang, H.: A positioning scheme combining location tracking with vision assisting for wireless sensor networks. J. Appl. Res. Technol. 11(2), 292–300 (2013)
Yun, X., Bachmann, E.R., Moore, H., Calusdian, J.: Self-contained position tracking of human movement using small inertial/magnetic sensor modules. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 2526–2533. IEEE (2007)
Zhang, W., Li, X., Wei, D., Ji, X., Yuan, H.: A foot-mounted PDR system based on IMU/EKF+ HMM+ ZUPT+ ZARU+ HDR+ compass algorithm. In: 2017 International conference on indoor positioning and indoor navigation (IPIN), pp. 1–5. IEEE (2017)
Zhuang, Y., Shen, Z., Syed, Z., Georgy, J., Syed, H., El-Sheimy, N.: Autonomous WLAN heading and position for smartphones. In: 2014 IEEE/ION Position, Location and Navigation Symposium-PLANS 2014, pp. 1113–1121. IEEE (2014)
Zhuang, Y., Syed, Z., Georgy, J., El-Sheimy, N.: Autonomous smartphone-based WIFI positioning system by using access points localization and crowdsourcing. Pervasive Mob. Comput. 18, 118–136 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zuo, J., Zhang, C., Shu, KI., Zhang, H. (2020). Localization Research Based on Low Cost Sensor. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-64243-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64242-6
Online ISBN: 978-3-030-64243-3
eBook Packages: Computer ScienceComputer Science (R0)