single-rb.php

JRM Vol.37 No.3 pp. 762-778
doi: 10.20965/jrm.2025.p0762
(2025)

Review:

Mobile Robot Navigation Based on Artificial Markers: A Systematic Mapping Study

Moteb Alghamdi*,† ORCID Icon, Ashraf Al-Marakeby** ORCID Icon, and Salah Abdel-Mageid*,** ORCID Icon

*Department of Computer Engineering, Collage of Computer Science and Engineering, Taibah University
Janadah Bin Umayyah Road, Tayba, Madinah 42353, Saudi Arabia

Corresponding author

**Systems and Computers Department, Faculty of Engineering, Al-Azhar University
Permanent Camp Street, Nasr City, Cairo 11884, Egypt

Received:
December 3, 2024
Accepted:
February 6, 2025
Published:
June 20, 2025
Keywords:
mobile robot, indoor navigation, artificial markers, marker detection
Abstract

Mobile robot navigation plays a vital role in robotics, allowing autonomous systems to function effectively across different environments. Artificial markers offer a dependable and efficient solution for localization and mapping, especially in structured environments. This systematic mapping study aims to thoroughly examine the existing body of literature, including research papers that explore mobile robot navigation techniques utilizing artificial markers. Our review systematically analyzes various aspects such as marker types, detection algorithms, and navigation strategies. The study aims to uncover research gaps, highlight emerging trends, and suggest potential future directions. By evaluating the strengths and limitations of current approaches, we strive to address key research questions related to marker-based navigation systems for mobile robots.

Artificial markers symbols used in the investigated 44 papers

Artificial markers symbols used in the investigated 44 papers

Cite this article as:
M. Alghamdi, A. Al-Marakeby, and S. Abdel-Mageid, “Mobile Robot Navigation Based on Artificial Markers: A Systematic Mapping Study,” J. Robot. Mechatron., Vol.37 No.3, pp. 762-778, 2025.
Data files:
References
  1. [1] B. Kitchenham and S. M. Charters, “Guidelines for performing systematic literature reviews in software engineering,” EBSE Technical Report, No.EBSE-2007-01, 2007.
  2. [2] C. Okoli, “A guide to conducting a standalone systematic literature review,” Communications of the Association for Information Systems, Vol.37, pp. 879-910, 2015. https://doi.org/10.17705/1CAIS.03743
  3. [3] M. Petticrew and H. Roberts, “Systematic Reviews in the Social Sciences: A Practical Guide,” John Wiley & Sonc, Inc., 2006. https://doi.org/10.1002/9780470754887
  4. [4] B. Kitchenham and P. Brereton, “A systematic review of systematic review process research in software engineering,” Information and Software Technology, Vol.55, No.12, pp. 2049-2075, 2013. https://doi.org/10.1016/j.infsof.2013.07.010
  5. [5] H. Cooper, “Research Synthesis and Meta-Analysis: A Step-by-Step Approach,” SAGE Publications, Inc., 2017. https://doi.org/10.4135/9781071878644
  6. [6] M. J. Grant and A. Booth, “A typology of reviews: An analysis of 14 review types and associated methodologies,” Health Information and Libraries J., Vol.26, No.2, pp. 91-108, 2009. https://doi.org/10.1111/j.1471-1842.2009.00848.x
  7. [7] X. Dong, K. Yuan, Z. Zhu, and F. Wen, “A hybrid method for robot navigation based on MR code landmark,” 8th World Congress on Intelligent Control and Automation, pp. 6676-6680, 2010. https://doi.org/10.1109/WCICA.2010.5554155
  8. [8] Y. Xu et al., “Design and recognition of monocular visual artificial landmark based on arc angle information coding,” 33rd Youth Academic Annual Conf. of Chinese Association of Automation, pp. 722-727, 2018. https://doi.org/10.1109/YAC.2018.8406466
  9. [9] Y. Li, S. Zhu, Y. Yu, and Z. Wang, “An improved graph-based visual localization system for indoor mobile robot using newly designed markers,” Int. J. of Advanced Robotic Systems, Vol.15, No.2, 2018. https://doi.org/10.1177/1729881418769191
  10. [10] V. Kroumov and K. Okuyama, “Localisation and position correction for mobile robot using artificial visual landmarks,” Int. J. of Advanced Mechatronic Systems, Vol.4, No.2, pp. 112-119, 2012. https://doi.org/10.1504/IJAMECHS.2012.048395
  11. [11] Y. Lei, X. Wang, L. Ren, and Z. Feng, “Research on artificial landmark recognition method based on omni-vision sensor,” 5th Int. Conf. on Intelligent Networks and Intelligent Systems, pp. 325-328, 2012. https://doi.org/10.1109/ICINIS.2012.68
  12. [12] Z. Taha and J. A. Mat-Jizat, “Landmark navigation in low illumination using omnidirectional camera,” 9th Int. Conf. on Ubiquitous Robots and Ambient Intelligence, pp. 262-265, 2012. https://doi.org/10.1109/URAI.2012.6462990
  13. [13] L. Cavanini et al., “A QR-code localization system for mobile robots: Application to smart wheelchairs,” 2017 European Conf. on Mobile Robots, 2017. https://doi.org/10.1109/ECMR.2017.8098667
  14. [14] S.-H. Bach, P.-B. Khoi, and S.-Y. Yi, “Application of QR code for localization and navigation of indoor mobile robot,” IEEE Access, Vol.11, pp. 28384-28390, 2023. https://doi.org/10.1109/ACCESS.2023.3250253
  15. [15] M. J. Edwards, M. P. Hayes, and R. D. Green, “High-accuracy fiducial markers for ground truth,” 2016 Int. Conf. on Image and Vision Computing New Zealand, 2016. https://doi.org/10.1109/IVCNZ.2016.7804461
  16. [16] S. Babu and S. Markose, “IoT enabled robots with QR code based localization,” 2018 Int. Conf. on Emerging Trends and Innovations in Engineering and Technological Research, 2018. https://doi.org/10.1109/ICETIETR.2018.8529028
  17. [17] S.-J. Lee, G. Tewolde, J. Lim, and J. Kwon, “QR-code based localization for indoor mobile robot with validation using a 3D optical tracking instrument,” 2015 IEEE Int. Conf. on Advanced Intelligent Mechatronics, pp. 965-970, 2015. https://doi.org/10.1109/AIM.2015.7222664
  18. [18] S. Wang et al., “CylinderTag: An accurate and flexible marker for cylinder-shape objects pose estimation based on projective invariants,” IEEE Trans. on Visualization and Computer Graphics, Vol.30, No.12, pp. 7486-7499, 2024. https://doi.org/10.1109/TVCG.2024.3350901
  19. [19] F. Bergamasco, A. Albarelli, L. Cosmo, E. Rodolà, and A. Torsello, “An accurate and robust artificial marker based on cyclic codes,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.38, No.12, pp. 2359-2373, 2016. https://doi.org/10.1109/TPAMI.2016.2519024
  20. [20] G. Li and Z. Jiang, “An artificial landmark design based on mobile robot localization and navigation,” 4th Int. Conf. on Intelligent Computation Technology and Automation, pp. 588-591, 2011. https://doi.org/10.1109/ICICTA.2011.597
  21. [21] Y.-C. Lee, Christiand, H. Chae, and S.-H. Kim, “Applications of robot navigation based on artificial landmark in large scale public space,” 2011 IEEE Int. Conf. on Robotics and Biomimetics, pp. 721-726, 2011. https://doi.org/10.1109/ROBIO.2011.6181371
  22. [22] G. Kim and E. M. Petriu, “Fiducial marker indoor localization with artificial neural network,” 2010 IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, pp. 961-966, 2010. https://doi.org/10.1109/AIM.2010.5695801
  23. [23] M. Zhou, J. Gui, B. Deng, D. Xu, and Y. Yan, “Fiducial marker-based metric initialization for monocular SLAM,” 5th Int. Conf. on Automation, Control and Robotics Engineering, pp. 630-635, 2020. https://doi.org/10.1109/CACRE50138.2020.9230158
  24. [24] Y. Song, B. Lian, and C. Li, “Indoor integrated navigation for differential wheeled robot using ceiling artificial landmark,” Proc. 2013 Int. Conf. on Mechatronic Sciences, Electric Engineering and Computer, pp. 828-831, 2013. https://doi.org/10.1109/MEC.2013.6885173
  25. [25] Z. Mikulová, F. Duchoň, M. Dekan, and A. Babinec, “Localization of mobile robot using visual system,” Int. J. of Advanced Robotic Systems, Vol.14, No.5, 2017. https://doi.org/10.1177/1729881417736085
  26. [26] J.-Y. Lee and W. Yu, “Robust self-localization of ground vehicles using artificial landmark,” 11th Int. Conf. on Ubiquitous Robots and Ambient Intelligence, pp. 303-307, 2014. https://doi.org/10.1109/URAI.2014.7057439
  27. [27] G. Lan, J. Wang, and W. Chen, “An improved indoor localization system for mobile robots based on landmarks on the ceiling,” 2016 IEEE Int. Conf. on Robotics and Biomimetics, pp. 1395-1400, 2016. https://doi.org/10.1109/ROBIO.2016.7866522
  28. [28] A. Alabbas, M. A. Cabrera, O. Alyounes, and D. Tsetserukou, “ArUcoGlide: A novel wearable robot for position tracking and haptic feedback to increase safety during human-robot interaction,” IEEE 28th Int. Conf. on Emerging Technologies and Factory Automation, 2023. https://doi.org/10.1109/ETFA54631.2023.10275727
  29. [29] A. Bousaid, T. Theodoridis, and S. Nefti-Meziani, “Introducing a novel marker-based geometry model in monocular vision,” 13th Workshop on Positioning, Navigation and Communications, 2016. https://doi.org/10.1109/WPNC.2016.7822857
  30. [30] R. Chand, A. Prasad, B. Sharma, and J. Vanualailai, “Landmark aided navigation of a point-mass robot via Lyapunov-based control scheme,” Asia-Pacific World Congress on Computer Science and Engineering, 2014. https://doi.org/10.1109/APWCCSE.2014.7053847
  31. [31] C. Buchner, P. Gsellmann, M. M. Merkumians, and G. Schitter, “Eye-in-hand pose estimation of industrial robots,” 49th Annual Conf. of the IEEE Industrial Electronics Society, 2023. https://doi.org/10.1109/IECON51785.2023.10312053
  32. [32] A. Tourani, H. Bavle, J. L. Sanchez-Lopez, R. M. Salinas, and H. Voos, “Marker-based visual SLAM leveraging hierarchical representations,” 2023 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3461-3467, 2023. https://doi.org/10.1109/IROS55552.2023.10341891
  33. [33] M. Beinhofer, J. Müller, and W. Burgard, “Near-optimal landmark selection for mobile robot navigation,” 2011 IEEE Int. Conf. on Robotics and Automation, pp. 4744-4749, 2011. https://doi.org/10.1109/ICRA.2011.5979871
  34. [34] B. Dzodzo, L. Han, X. Chen, H. Qian, and Y. Xu, “Realtime 2D code based localization for indoor robot navigation,” 2013 IEEE Int. Conf. on Robotics and Biomimetics, pp. 486-492, 2013. https://doi.org/10.1109/ROBIO.2013.6739507
  35. [35] M. Zhang et al., “Visual-marker-inertial fusion localization system using sliding window optimization,” 33rd Chinese Control and Decision Conf., pp. 3566-3572, 2021. https://doi.org/10.1109/CCDC52312.2021.9602344
  36. [36] Z. Jiang et al., “A mobile robot indoor positioning system based on ArUco array and extended Kalman filter,” 2023 China Automation Congress (CAC), pp. 562-567, 2023. https://doi.org/10.1109/CAC59555.2023.10450326
  37. [37] G. Lei, X. Xu, X. Yu, Y. Wang, and T. Qu, “An indoor localization method for humanoid robot based on artificial landmark,” 5th Int. Conf. on Instrumentation and Measurement, Computer, Communication and Control, pp. 1854-1857, 2015. https://doi.org/10.1109/IMCCC.2015.394
  38. [38] N. Strisciuglio, M. L. Vallina, N. Petkov, and R. M. Salinas, “Camera localization in outdoor garden environments using artificial landmarks,” 2018 IEEE Int. Work Conf. on Bioinspired Intelligence, 2018. https://doi.org/10.1109/IWOBI.2018.8464139
  39. [39] W. Serna, G. Daza, and N. Izquierdo, “Planar approximation of three-dimensional data for refinement of marker-based tracking algorithm,” 2016 XXI Symp. on Signal Processing, Images and Artificial Vision, 2016. https://doi.org/10.1109/STSIVA.2016.7743362
  40. [40] C. d. S. Fernandes, M. F. M. Campos, and L. Chaimowicz, “A low-cost localization system based on artificial landmarks,” 2012 Brazilian Robotics Symp. and Latin American Robotics Symp, pp. 109-114, 2012. https://doi.org/10.1109/SBR-LARS.2012.25
  41. [41] Y. Xie et al., “A4LidarTag: Depth-based fiducial marker for extrinsic calibration of solid-state Lidar and camera,” IEEE Robotics and Automation Letters, Vol.7, No.3, pp. 6487-6494, 2022. https://doi.org/10.1109/LRA.2022.3173033
  42. [42] Q. Zeng et al., “An indoor 2-D LiDAR SLAM and localization method based on artificial landmark assistance,” IEEE Sensors J., Vol.24, No.3, pp. 3681-3692, 2024. https://doi.org/10.1109/JSEN.2023.3341832
  43. [43] Y.-C. Lee, Christiand, H. Chae, and W. Yu, “Artificial landmark map building method based on grid SLAM in large scale indoor environment,” 2010 IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 4251-4256, 2010. https://doi.org/10.1109/ICSMC.2010.5642489
  44. [44] M. Li et al., “Artificial landmark positioning system using omnidirectional vision for agricultural vehicle navigation,” 2nd Int. Conf. on Intelligent System Design and Engineering Application, pp. 665-669, 2012. https://doi.org/10.1109/ISdea.2012.730
  45. [45] M. Beinhofer, H. Kretzschmar, and W. Burgard, “Deploying artificial landmarks to foster data association in simultaneous localization and mapping,” 2013 IEEE Int. Conf. on Robotics and Automation, pp. 5235-5240, 2013. https://doi.org/10.1109/ICRA.2013.6631325
  46. [46] T. Almeida, V. Santos, B. Lourenço, and P. Fonseca, “Detection of data matrix encoded landmarks in unstructured environments using deep learning,” 2020 IEEE Int. Conf. on Autonomous Robot Systems and Competitions, pp. 74-80, 2020. https://doi.org/10.1109/ICARSC49921.2020.9096211
  47. [47] J.-K. Huang, S. Wang, M. Ghaffari, and J. W. Grizzle, “LiDARTag: A real-time fiducial tag system for point clouds,” IEEE Robotics and Automation Letters, Vol.6, No.3, pp. 4875-4882, 2021. https://doi.org/10.1109/LRA.2021.3070302
  48. [48] M. Beinhofer, J. Müller, A. Krause, and W. Burgard, “Robust landmark selection for mobile robot navigation,” 2013 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2637-2643, 2013. https://doi.org/10.1109/IROS.2013.6696728
  49. [49] B. P. E. Alvarado Vasquez, R. Gonzalez, F. Matia, and P. De La Puente, “Sensor fusion for tour-guide robot localization,” IEEE Access, Vol.6, pp. 78947-78964, 2018. https://doi.org/10.1109/ACCESS.2018.2885648
  50. [50] L. E. Ortiz-Fernandez, E. V. Cabrera-Avila, B. M. F. da Silva, and L. M. G. Gonçalves, “Smart artificial markers for accurate visual mapping and localization” Sensors, Vol.21, No.2, Article No.625, 2021. https://doi.org/10.3390/s21020625

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 20, 2025