Map-merging using maximal empty rectangles in a multi-robot SLAM process


A map-merging algorithm is proposed where reduced element maps are applied instead of grid maps and the maximal empty rectangles are applied as their features. Simultaneous localization and mapping (SLAM) refer to the process where a robot provides the environment map without any knowledge about its own position. Due to error accumulation, required time, saving lives and reasons alike, applying a single robot in the SLAM process is not justified. In such applications, many robots are to be applied in the SLAM process in a parallel sense. The map-merging process is one of the challenging topics in a multi-robot simultaneous localization and mapping process in producing a global map of the environment. In this study, a centralized algorithm is introduced for map-merging based on maximal empty rectangles as the features of local maps without any knowledge about robots’ initial or relative positions. Three examples and one experiment are applied in validating the performance of this newly proposed algorithm. The obtained results indicate that this algorithm can merge local maps with small overlapping areas in relation to the whole map, subject to multiple sources of error due to the difference in scales, diversity of agents applied and measurement noise.

This is a preview of subscription content, log in to check access.


  1. [1]

    J. E. Guivant and E. M. Nebot, Optimization of the simultaneous localization and map-building algorithm for realtime implementation, IEEE Transactions on Robotics and Automation, 17 (3) (2001) 242–257.

    Article  Google Scholar 

  2. [2]

    M. Montemerlo et al., FastSLAM 20: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico (2003).

    Google Scholar 

  3. [3]

    K. P. Murphy, Bayesian map learning in dynamic environments, Advances in Neural Information Processing Systems (2000) 1015–1021.

    Google Scholar 

  4. [4]

    M. A. Paskin, Thin junction tree filters for simultaneous localization and mapping, Int. Joint Conf. on Artificial Intelligence (2003).

    Google Scholar 

  5. [5]

    S. Thrun et al., Simultaneous mapping and localization with sparse extended information filters: Theory and initial results, Algorithmic Foundations of Robotics V (2004) 363–380.

    Google Scholar 

  6. [6]

    J. W. Fenwick, P. M. Newman and J. J. Leonard, Cooperative concurrent mapping and localization, 2002 IEEE International Conference on Robotics and Automation, Washington, DC, USA (2002) 1810–1817.

    Google Scholar 

  7. [7]

    K. Konolige et al., Centibots: Very large scale distributed robotic teams, Springer Tracts in Advanced Robotics, 21 (1) (2006) 131–140.

    Article  Google Scholar 

  8. [8]

    S. Thrun, A probabilistic on-line mapping algorithm for teams of mobile robots, The International Journal of Robotics Research, 20 (5) (2001) 335–363.

    Article  Google Scholar 

  9. [9]

    S. B. Williams, G. Dissanayake and H. Durrant-Whyte, Towards multi-vehicle simultaneous localisation and mapping, Proceedings 2002 IEEE International Conference on Robotics and Automation, Washington, DC, USA (2002) 2743–2748.

    Google Scholar 

  10. [10]

    W. Rone and P. Ben-Tzvi, Mapping, localization and motion planning in mobile multi-robotic systems, Robotica, 31 (1) (2013) 1–23.

    Article  Google Scholar 

  11. [11]

    S. Thrun and Y. Liu, Multi-robot SLAM with sparse extended information filers, Robotics Research, 15 (1) (2005) 254–266.

    Google Scholar 

  12. [12]

    S. Carpin, A. Birk and V. Jucikas, On map merging, International Journal of Robotics and Autonomous Systems, 53 (1) (2005) 1–14.

    Article  Google Scholar 

  13. [13]

    A. Birk and S. Carpin, Merging occupancy grid maps from multiple robots, Proceedings of the IEEE (2006) 1384–1397.

    Google Scholar 

  14. [14]

    S. Saeedi et al., Neural network-based multiple robot simultaneous localization and mapping, IEEE Transactions on Neural Networks, 22 (12) (2011) 2376–2387.

    Article  Google Scholar 

  15. [15]

    S. Saeedi et al., Efficient map merging using a probabilistic generalized Voronoi diagram, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (2012) 4419–4424.

    Google Scholar 

  16. [16]

    P. Dinnissen, S. N. Givigi and H. M. Schwartz, Map merging of multi-robot SLAM using reinforcement learning, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2012) 53–60.

    Google Scholar 

  17. [17]

    H. Li et al., Multivehicle cooperative local mapping: A methodology based on occupancy grid map merging, IEEE Transactions on Intelligent Transportation Systems, 15 (5) (2014) 2089–2100.

    Article  Google Scholar 

  18. [18]

    J. Park et al., Map merging of rotated, corrupted, and different scale maps using rectangular features, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS) (2016) 535–543.

    Google Scholar 

  19. [19]

    J. Park, A reduced element map representation and applications: Map merging, path planning, and target interception, Ph. D. Dissertation, Department of Aerospace Engineering, Auburn University (2017).

    Google Scholar 

  20. [20]

    A. Howard and N. Roy, Radish: The Robotics Data Set Repository (2003).

    Google Scholar 

  21. [21]

    H. Li, M. A. Lavin and R. J. Le Master, Fast Hough transform: A hierarchical approach, Computer Vision, Graphics, and Image Processing, 36 (2-3) (1986) 139–161.

    Article  Google Scholar 

  22. [22]

    J. B. Burns, A. R. Hanson and E. M. Riseman, Extracting straight lines, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 8 (4) (1986) 425–455.

    Article  Google Scholar 

  23. [23]

    D. S. Guru, B. H. Shekar and P. Nagabhushan, A simple and robust line detection algorithm based on small eigenvalue analysis, Pattern Recognition Letters, 25 (1) (2004) 1–13.

    Article  Google Scholar 

  24. [24]

    Y.-S. Lee, H.-S. Koo and C.-S. Jeong, A straight line detection using principal component analysis, Pattern Recognition Letters, 27 (14) (2006) 1744–1754.

    Article  Google Scholar 

  25. [25]

    J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 8 (6) (1986) 679–698.

    Article  Google Scholar 

  26. [26]

    Line Detection by Hough Transformation (2009)

  27. [27]

    J. G. Ahn and H. S. Jeon, R-map: A hybrid map created by maximal rectangles, ICCAS 2010, South Korea (2010) 1336–1339.

    Google Scholar 

Download references


The author would like to express his gratitude to the experts and students in the Advanced Mechatronics and Robotics Laboratory (ARMLAB) at the Mechanical Engineering Department of Isfahan University of Technology for their generous contribution in preparing the facilities and for providing the required environment. Some datasets were obtained from the Robotics Data Set Repository (Radish). Thanks go to Maxim Batalin, Andrew Howard and Patrick Beeson for providing the data.

Author information



Corresponding author

Correspondence to Shahram Hadian Jazi.

Additional information

Recommended by Editor Ja Choon Koo

Shahram Hadian Jazi received his B.Sc., M.Sc. and Ph.D. in Mechanical Engineering from Isfahan University of Technology, Isfahan, Iran. And now he is a faculty member in the Department of Mechanical Engineering, University of Isfahan, Isfahan, Iran. His research interests are in robotics (analysis, design, and manufac-turing), mechatronics and control of dynamical systems.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hadian Jazi, S. Map-merging using maximal empty rectangles in a multi-robot SLAM process. J Mech Sci Technol 34, 2573–2583 (2020).

Download citation


  • Multi-robot SLAM
  • Map-merging
  • Maximal empty rectangles
  • Reduced element map