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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Abstract

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein offer interesting insights and shed light on an alternative solution in introducing a robust positioning system.

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Data and experimental materials used in this study are available upon request.

References

  • Bailey T, Durrant-Whyte H (2006) Simultaneous localization and mapping (slam): Part II. IEEE Rob Autom Mag 13(3):108–117

    Article  Google Scholar 

  • Biswas J, Veloso M (2010) Wifi localization and navigation for autonomous indoor mobile robots. In: IEEE international conference on robotics and automation. IEEE 2010, pp 4379–4384

  • Boonsriwai S, Apavatjrut A (2013) Indoor Wi-Fi localization on mobile devices. In: 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, IEEE, pp. 1–5

  • Bresson G, Alsayed Z, Yu L, Glaser S (2017) Simultaneous localization and mapping: a survey of current trends in autonomous driving. IEEE Trans Intell Veh 2(3):194–220

    Article  Google Scholar 

  • Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ (2016) Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans Rob 32(6):1309–1332

    Article  Google Scholar 

  • Chen X, Sun H, Zhang H (2019) A new method of simultaneous localization and mapping for mobile robots using acoustic landmarks. Appl Sci 9(7):1352

    Article  Google Scholar 

  • Cotera P, Velazquez M, Cruz D, Medina L, Bandala M (2016) Indoor robot positioning using an enhanced trilateration algorithm. Int J Adv Rob Syst 13(3):110

    Article  Google Scholar 

  • Debeunne C, Vivet D (2020) A review of visual-lidar fusion based simultaneous localization and mapping. Sensors 20(7):2068

    Article  Google Scholar 

  • Doh N, Choset H, Chung WK (2003) Accurate relative localization using odometry. In: 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), Vol. 2, IEEE, pp. 1606–1612

  • Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: part I. IEEE Rob Autom Mag 13(2):99–110

    Article  Google Scholar 

  • Fuentes-Pacheco J, Ruiz-Ascencio J, Rendón-Mancha JM (2015) Visual simultaneous localization and mapping: a survey. Artif Intell Rev 43(1):55–81

    Article  Google Scholar 

  • Guan W, Chen S, Wen S, Tan Z, Song H, Hou W (2020) High-accuracy robot indoor localization scheme based on robot operating system using visible light positioning. IEEE Photon J 12(2):1–16

    Article  Google Scholar 

  • Jetto L, Longhi S, Venturini G (1999) Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots. IEEE Trans Robot Autom 15(2):219–229

    Article  Google Scholar 

  • Kim J, Chung W (2018) Robust localization of mobile robots considering reliability of lidar measurements. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 6491–6496

  • Kotaru M, Joshi K, Bharadia D, Katti S (2015) Spotfi: decimeter level localization using Wi-Fi. In: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, pp. 269–282

  • Monica S, Ferrari G (2021) Robust UWB-based localization with application to automated guided vehicles. Adv Intell Syst 3(4):2000083

    Article  Google Scholar 

  • Nguyen VT, Jeong MS, Ahn SM, Moon SB, Baik SW (2007) A robust localization method for mobile robots based on ceiling landmarks. In: International Conference on Modeling Decisions for Artificial Intelligence, Springer, pp. 422–430

  • Park S, Hashimoto S (2008) Indoor localization for autonomous mobile robot based on passive rfid. In IEEE international conference on robotics and biomimetics. IEEE 2009, pp 1856–1861

  • Ridolfi M, Kaya A, Berkvens R, Weyn M, Joseph W, Poorter ED (2021) Self-calibration and collaborative localization for UWB positioning systems: a survey and future research directions. ACM Comput Surv (CSUR) 54(4):1–27

    Article  Google Scholar 

  • Saeedi S, Trentini M, Seto M, Li H (2016) Multiple-robot simultaneous localization and mapping: a review. J Field Robot 33(1):3–46

    Article  Google Scholar 

  • Sharifi H, Kumar A, Alam F, Arif KM (2016) Indoor localization of mobile robot with visible light communication. In: 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), IEEE, pp. 1–6

  • Shi D, Mi H (2021) Study on UWB-based low-cost and precise localization method for mobile robots. Curr Approaches Sci Technol Res 2:90–96

    Article  Google Scholar 

  • Thomas F, Ros L (2005) Revisiting trilateration for robot localization. IEEE Trans Rob 21(1):93–101

    Article  Google Scholar 

  • Wang J, Li C, Li B, Pang C, Fang Z (2021) High-precision and robust localization system for mobile robots in complex and large-scale indoor scenes. Int J Adv Rob Syst 18(5):17298814211047690

    Google Scholar 

  • Wolf DF, Sukhatme GS (2005) Mobile robot simultaneous localization and mapping in dynamic environments. Auton Robot 19(1):53–65

    Article  Google Scholar 

  • Xu D, Han L, Tan M, Li YF (2009) Ceiling-based visual positioning for an indoor mobile robot with monocular vision. IEEE Trans Ind Electron 56(5):1617–1628

    Article  Google Scholar 

  • Yasir M, Ho S-W, Vellambi BN (2014) Indoor positioning system using visible light and accelerometer. J Lightwave Technol 32(19):3306–3316

    Article  Google Scholar 

  • Zwirello L, Schipper T, Harter M, Zwick T (2012) UWB localization system for indoor applications: concept, realization and analysis. J Electr Comput Eng. https://doi.org/10.1155/2012/849638

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Graduate Institute of Architecture, National Yang Ming Chiao Tung University, Hsinchu, Taiwan R.O.C.

    Shih-Yuan Wang

  2. Department of Electronic Engineering, Feng Chia University, 40724, Taichung, Taiwan R.O.C.

    Che-Ming Li & Sze-Teng Liong

  3. School of Architecture, Feng Chia University, 40724, Taichung, Taiwan R.O.C.

    Yu-Ting Sheng & Yee-Siang Gan

  4. Department of Applied Mathematics, National University of Tainan, Tainan, Taiwan R.O.C.

    Yen-Chang Huang

  5. Faculty of Computer Science & Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Gen-Bing Liong

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  1. Shih-Yuan Wang
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  2. Che-Ming Li
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  3. Sze-Teng Liong
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  4. Yu-Ting Sheng
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  5. Yen-Chang Huang
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  7. Yee-Siang Gan
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Correspondence to Yee-Siang Gan.

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Wang, SY., Li, CM., Liong, ST. et al. AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors. J Ambient Intell Human Comput 15, 2277–2293 (2024). https://doi.org/10.1007/s12652-024-04755-5

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  • DOI: https://doi.org/10.1007/s12652-024-04755-5

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