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A low-cost indoor positioning system based on data-driven modeling for robotics research and education

Published online by Cambridge University Press:  11 May 2023

Junlin Ou Open the ORCID record for Junlin Ou [Opens in a new window] ,
Seong Hyeon Hong ,
Tristan Kyzer ,
Haizhou Yang ,
Xianlian Zhou  and
Yi Wang
Junlin Ou
Affiliation:
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Seong Hyeon Hong
Affiliation:
Department of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA
Tristan Kyzer
Affiliation:
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Haizhou Yang
Affiliation:
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Xianlian Zhou
Affiliation:
Department of Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Yi Wang*
Affiliation:
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
*
Corresponding author: Yi Wang; Email: yiwang@cec.sc.edu
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Abstract

This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

Keywords

ArUco markerOpenCVindoor positioning systemimage processingdata-driven model
Type
Research Article
Information
Robotica , Volume 41 , Issue 9 , September 2023 , pp. 2648 - 2667
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Jiménez, A. R. and Seco, F., “Comparing Decawave and Bespoon UWB Location Systems: Indoor/Outdoor Performance Analysis,” 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2016 Oct 4 (IEEE, 2016) pp. 1–8.CrossRefGoogle Scholar
Amsters, R., Demeester, E., Stevens, N., Lauwers, Q. and Slaets, P., “Evaluation of Low-Cost/High-Accuracy Indoor Positioning Systems,” Proceedings of the 2019 International Conference on Advances in Sensors, Actuators, Metering and Sensing (ALLSENSORS), Athens, Greece 2019 Feb 24 (2019) pp. 24–28.Google Scholar
Kuang, J., Niu, X., Zhang, P. and Chen, X., “Indoor positioning based on pedestrian dead reckoning and magnetic field matching for smartphones,” Sensors 18(12), 4142 (2018).CrossRefGoogle ScholarPubMed
Ju, H., Park, S. Y. and Park, C. G., “A smartphone-based pedestrian dead reckoning system with multiple virtual tracking for indoor navigation,” IEEE Sens. J. 18(16), 6756–6764 (2018).CrossRefGoogle Scholar
Ali, R., Liu, R., Nayyar, A., Qureshi, B. and Cao, Z., “Tightly coupling fusion of UWB ranging and IMU pedestrian dead reckoning for indoor localization,” IEEE Access 9, 164206–164222 (2021).CrossRefGoogle Scholar
Mareedu, M., Kothacheruvu, S. and Raghunath, S., “Indoor Navigation Using Ultra-Wideband Indoor Positioning System (IPS),” AIP Conference Proceedings, 2021 Dec 1, AIP Publishing LLC, vol. 2407 (2021) pp. 020022.Google Scholar
Zhang, Z., Lee, M. and Choi, S., “Deep-learning-based wi-fi indoor positioning system using continuous CSI of trajectories,” Sensors 21(17), 5776 (2021).CrossRefGoogle ScholarPubMed
Bencak, P., Hercog, D. and Lerher, T., “Indoor positioning system based on bluetooth low energy technology and a nature-inspired optimization algorithm,” Electronics 11(3), 308 (2022).CrossRefGoogle Scholar
Zhou, T., Ku, J., Lian, B. and Zhang, Y., “Indoor positioning algorithm based on improved convolutional neural network,” Neural Comput. Appl. 34(9), 6787–6798 (2022).CrossRefGoogle Scholar
Yan, S., Su, Y., Sun, A., Ji, Y., Xiao, J. and Chen, X., “Low-Cost and Lightweight Indoor Positioning Based on Computer Vision,” 2022 4th Asia Pacific Information Technology Conference 2022 Jan 14 (2022) pp. 169–175.Google Scholar
Ng, Z. Y., Indoor-Positioning for Warehouse Mobile Robots Using Computer Vision (Doctoral dissertation, UTAR).Google Scholar
Kunhoth, J., Karkar, A., Al-Maadeed, S. and Al-Ali, A., “Indoor positioning and wayfinding systems: A survey,” Hum.-Centric Comput. Infor. Sci. 10(1), 1–41 (2020).Google Scholar
Kuang, J., Niu, X. and Chen, X., “Robust pedestrian dead reckoning based on MEMS-IMU for smartphones,” Sensors 18(5), 1391 (2018).CrossRefGoogle ScholarPubMed
Wang, B., Liu, X., Yu, B., Jia, R. and Gan, X., “Pedestrian dead reckoning based on motion mode recognition using a smartphone,” Sensors 18(6), 1811 (2018).CrossRefGoogle ScholarPubMed
Liu, X., Jiao, Z., Chen, L., Pan, Y., Lu, X. and Ruan, Y., “An enhanced pedestrian dead reckoning aided with DTMB signals,” IEEE Trans. Broadcast 68(2), 407–413 (2022).CrossRefGoogle Scholar
Ruiz, A. R. and Granja, F. S., “Comparing ubisense, bespoon, and decawave uwb location systems: Indoor performance analysis,” IEEE Trans. Inst. Meas. 66(8), 2106–2117 (2017).CrossRefGoogle Scholar
Sthapit, P., Gang, H. S. and Pyun, J. Y., “Bluetooth Based Indoor Positioning Using Machine Learning Algorithms,” 2018 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 2018 Jun 24 (IEEE, 2018) pp. 206–212.CrossRefGoogle Scholar
Han, Z., Wang, Z., Huang, H., Zhao, L. and Su, C., “WiFi-based indoor positioning and communication: Empirical model and theoretical analysis,” Wireless Commun. Mobile Comput., 2022 1–12 (2022).Google Scholar
Jia, S., Ma, L., Yang, S. and Qin, D., “A novel visual indoor positioning method with efficient image deblurring,” IEEE Trans. Mobile Comput., 1–1 (2022).Google Scholar
Liu, X., Huang, H. and Hu, B., “Indoor visual positioning method based on image features,” Sens. Mater. 34(1), 337–348 (2022).Google Scholar
Li, Y., Ghassemlooy, Z., Tang, X., Lin, B. and Zhang, Y., “A VLC smartphone camera based indoor positioning system,” IEEE Photonic Tech. Lett. 30(13), 1171–1174 (2018).CrossRefGoogle Scholar
Kalaitzakis, M., Cain, B., Carroll, S., Ambrosi, A., Whitehead, C. and Vitzilaios, N., “Fiducial markers for pose estimation,” J. Intell. Rob. Syst. 101(4), 1–26 (2021).CrossRefGoogle Scholar
La Delfa, G. C., Monteleone, S., Catania, V., De Paz, J. F. and Bajo, J., “Performance analysis of visualmarkers for indoor navigation systems,” Front. Inform. Technol. Electron. Eng. 17(8), 730–740 (2016).CrossRefGoogle Scholar
Arun, K. S., Huang, T. S. and Blostein, S. D., “Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Sep(5), 698–700 (1987).CrossRefGoogle Scholar
Kurobe, A., Sekikawa, Y., Ishikawa, K. and Corsnet, S. H., “3d point cloud registration by deep neural network,” IEEE Robot. Autom. Lett. 5(3), 3960–3966 (2020).CrossRefGoogle Scholar
Sobester, A., Forrester, A. and Keane, A., Engineering Design Via Surrogate Modelling: A Practical Guide (John Wiley & Sons, 2008).Google Scholar
Kato, N., Mao, B., Tang, F., Kawamoto, Y. and Liu, J., “Ten challenges in advancing machine learning technologies toward 6G,” IEEE Wireless Commun. 27(3), 96–103 (2020).CrossRefGoogle Scholar
Yang, H., Hong, S. H., Wang, G. and Wang, Y., “Multi-fidelity reduced-order model for GPU-enabled microfluidic concentration gradient design,” Eng. Comput. 1–19 (2022).Google Scholar
Dietrich, C. R. and Osborne, M. R., “Estimation of covariance parameters in kriging via restricted maximum likelihood,” Math. Geol. 23(1), 119–135 (1991).CrossRefGoogle Scholar
Lophaven, S. N., Nielsen, H. B. and Søndergaard, J.. DACE: A Matlab Kriging Toolbox (IMM, Informatics and Mathematical Modelling, The Technical University of Denmark, Lyngby, Denmark, 2002).Google Scholar
Couckuyt, I., Dhaene, T. and Demeester, P., “ooDACE toolbox: A flexible object-oriented Kriging implementation,” J. Mach. Learn. Res. 15, 3183–3186 (2014).Google Scholar
Kyzer, T., Instrumentation and Experimentation Development for Robotic Systems (Doctoral dissertation, University of South Carolina).Google Scholar
Hong, S. H., Ou, J. and Wang, Y., “Physics-guided neural network and GPU-accelerated nonlinear model predictive control for quadcopter,” Neural Comput. Appl. 13(1), 1–21 (2022).Google Scholar
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