AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (4.2 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Anchor Self-Localization Algorithm Based on UWB Ranging and Inertial Measurements

Qin ShiSihao ZhaoXiaowei Cui*( )Mingquan LuMengdi Jia
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Show Author Information

Abstract

Localization systems utilizing Ultra-WideBand (UWB) have been widely used in dense urban and indoor environments. A moving UWB tag can be located by ranging to fixed UWB anchors whose positions are surveyed in advance. However, manually surveying the anchors is typically a dull and time-consuming process and prone to artificial errors. In this paper, we present an accurate and easy-to-use method for UWB anchor self-localization, using the UWB ranging measurements and readings from a low-cost Inertial Measurement Unit (IMU). The locations of the anchors are automatically estimated by freely moving the tag in the environment. The method is inspired by the Simultaneous Localization And Mapping (SLAM) technique used by the robotics community. A tightly-coupled Error-State Kalman Filter (ESKF) is utilized to fuse UWB and inertial measurements, producing UWB anchor position estimates and six Degrees of Freedom (6DoF) tag pose estimates. Simulated experiments demonstrate that our proposed method enables accurate self-localization for UWB anchors and smooth tracking of the tag.

References

[1]
Liu H., Darabi H., Banerjee P., and Liu J., Survey of wireless indoor positioning techniques and systems, IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., vol. 37, no. 6, pp. 1067-1080, 2007.
[2]
Gezici S., Tian Z., Giannakis G. B., Kobayashi H., Molisch A. F., Poor H. V., and Sahinoglu Z., Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks, IEEE Signal Proc. Mag., vol. 22, no. 4, pp. 70-84, 2005.
[3]
Zwirello L., Janson M., Ascher C., Schwesinger U., Trommer G. F., and Zwick T., Localization in industrial halls via ultra-wideband signals, in Proc. 2010 7th Workshop on Positioning, Navigation and Communication, Dresden, Germany, 2010, pp. 144-149.
[4]
Qin Y. Q., Wang F., and Zhou C. J., A distributed UWB-based localization system in underground mines, J. Net., vol. 10, no. 3, pp. 134-140, 2015.
[5]
Nguyen T. M., Zaini A. H., Guo K. X., and Xie L. H., An ultra-wideband-based multi-UAV localization system in GPS-denied environments, in Int. Micro Air Vehicle Conf. Competition, Beijing, China, 2016.
[6]
Bharadwaj R., Swaisaenyakorn S., Parini C. G., Batchelor J. C., and Alomainy A., Impulse radio ultra-wideband communications for localization and tracking of human body and limbs movement for healthcare applications, IEEE Trans. Anten. Propag., vol. 65, no. 12, pp. 7298-7309, 2017.
[7]
Guo K. X., Qiu Z. R., Miao C. X., Zaini A. H., Chen C. L., Meng W., and Xie L. H., Ultra-wideband-based localization for quadcopter navigation, Unmanned Syst., vol. 4, no. 1, pp. 23-34, 2016.
[8]
Guvenc I., Chong C. C., and Watanabe F., Joint TOA estimation and localization technique for UWB sensor network applications, in Proc. 2007 IEEE 65th Vehicular Technology Conf. VTC2007-Spring, Dublin, Ireland, 2007, pp. 1574-1578.
[9]
Zhang C., Kuhn M., Merkl B., Fathy A. E., and Mahfouz M., Accurate UWB indoor localization system utilizing time difference of arrival approach, in Proc. 2006 IEEE Radio and Wireless Symp., San Diego, CA, USA, 2006, pp. 515-518.
[10]
Xu J., Ma M. D., and Law C. L., Position estimation using UWB TDOA measurements, in Proc. 2006 IEEE Int. Conf. Ultra-WideBand (ICUWB), Waltham, MA, USA, 2006, pp. 605-610.
[11]
Tiemann J., Schweikowski F., and Wietfeld C., Design of an UWB indoor-positioning system for UAV navigation in GNSS-denied environments, in Proc. 2015 Int. Conf. Indoor Positioning Indoor Navigation (IPIN), Banff, Canada, 2015, pp. 1-7.
[12]
Wang J., Ghosh R. K., and Das S. K., A survey on sensor localization, J. Control Theory Appl., vol. 8, no. 1, pp. 2-11, 2010.
[13]
Hol J. D., Schön T. B., and Gustafsson F., Ultra-wideband calibration for indoor positioning, in Proc. 2010 IEEE Int. Conf. Ultra-WideBand (ICUWB), Nanjing, China, 2010, pp. 1-4.
[14]
Kok M., Hol J. D., and Schön T. B., Indoor positioning using ultrawideband and inertial measurements, IEEE Trans. Veh. Technol., vol. 64, no. 4, pp. 1293-1303, 2015.
[15]
Henderson D. M., Euler angles, quaternions, and transformation matrices for space shuttle analysis, https://ntrs.nasa.gov/search.jsp?R=19770019231, 1977.
[16]
Dissanayake M. W. M. G., Newman P., Clark S., Durrant-Whyte H. F., and Csorba M., A solution to the simultaneous localization and map building (SLAM) problem, IEEE Trans. Rob. Autom., vol. 17, no. 3, pp. 229-241, 2001.
[17]
Durrant-Whyte H. and Bailey T., Simultaneous localization and mapping: Part I, IEEE Robot. Autom. Mag., vol. 13, no. 2, pp. 99-110, 2006.
[18]
Bailey T. and Durrant-Whyte H., Simultaneous localization and mapping: Part II, IEEE Robot. Autom. Mag., vol. 13, no. 3, pp. 108-117, 2006.
[19]
Cadena C., Carlone L., Carrillo H., Latif Y., Scaramuzza D., Neira J., Reid I., and Leonard J. J., Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age, IEEE Trans. Robot., vol. 32, no. 6, pp. 1309-1332, 2016.
[20]
Smith R., Self M., and Cheeseman P., A stochastic map for uncertain spatial relationships, in Proc. 4th Int. Symp. Robotics Research, Santa Clara, CA, USA, 1987, pp. 467-474.
[21]
Solà J., Simulataneous localization and mapping with the extended Kalman filter, http://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf, 2013.
[22]
Hol J. D., Dijkstra F., Luinge H., and Schön T. B., Tightly coupled UWB/IMU pose estimation, in Proc. 2009 IEEE Int. Conf. Ultra-Wideband (ICUWB), Vancouver, Canada, 2009, pp. 688-692.
[23]
Zwirello L., Li X. Y., Zwick T., Ascher C., Werling S., and Trommer G. F., Sensor data fusion in UWB-supported inertial navigation systems for indoor navigation, in 2013 IEEE Int. Conf. Robotics and Automation, Karlsruhe, Germany, 2013, pp. 3154-3159.
[24]
Mueller M. W., Hamer M., and D’Andrea R., Fusing ultra-wideband range measurements with accelerometers and rate gyroscopes for quadrocopter state estimation, in 2015 IEEE Int. Conf. Robotics and Automation (ICRA), Seattle, WA, USA, 2015, pp. 1730-1736.
[25]
Maybeck P. S. and Siouris G. M., Stochastic models, estimation, and control, IEEE Trans. Syst. Man Cybern., vol. 10, no. 5, p. 282, 1980.
[26]
Farrell J. A., Aided Navigation: GPS with High Rate Sensors. New York, NY, USA: McGraw-Hill, 2008, pp. 354–377.
[27]
Neirynck D., Luk E., and McLaughlin M., An alternative double-sided two-way ranging method, in 2016 13th Workshop on Positioning, Navigation and Communicatins (WPNC), Bremen, Germany, 2016, pp. 1-4.
[28]
Solà J., Quaternion kinematics for the error-state Kalman filter, arXiv preprint arXiv: 1711.02508, 2017.
Tsinghua Science and Technology
Pages 728-737
Cite this article:
Shi Q, Zhao S, Cui X, et al. Anchor Self-Localization Algorithm Based on UWB Ranging and Inertial Measurements. Tsinghua Science and Technology, 2019, 24(6): 728-737. https://doi.org/10.26599/TST.2018.9010102

805

Views

98

Downloads

53

Crossref

N/A

Web of Science

69

Scopus

11

CSCD

Altmetrics

Received: 16 April 2018
Revised: 24 April 2018
Accepted: 25 April 2018
Published: 05 December 2019
© The author(s) 2019
Return