Simultaneously merging multi-robot grid maps at different resolutions

  • Zutao Jiang
  • Jihua Zhu
  • Congcong Jin
  • Siyu Xu
  • Yiqiong Zhou
  • Shanmin Pang


For an unknown GPS-denied environment, multiple robots can explore various areas of the same environment and build local maps for these areas. Accordingly, it is essential to merge these local maps of different robots into one global map. For grid map merging, this paper proposes an effective approach, which can simultaneously merge multi-robot grid maps at different resolutions. First, we present a scaling pairwise method for merging grid map pairs. Usually, a selected grid map is initialized as the seed map, which is then sequentially updated by performing scaling pairwise merging between itself and other input grid maps. Afterwards, map augmentation and feature fusion strategy is proposed to alternatively integrate two local maps into one initial global map. To address the accumulated error, a coarse-to-fine method is introduced to refine the initial global map and obtain the accurate global map. Experimental results conducted on real robot data sets demonstrate that the proposed approach can merge multiple grid maps simultaneously at different resolutions with good performances.


Multi-robot systems Different resolution map merging Iterative closet point Image registration 



This work was supported in part by the key projects of Trico-Robot plan of NSFC under Grant No. 91748208, the National Natural Science Foundation of China under Grant nos. 61573273 and 91648121, the Natural Science Foundation of Shaanxi Province of China under Grant no. 2015JM6301, the Fundamental Research Funds for Central Universities under Grant No. xjj2018214


  1. 1.
    Birk A, Carpin S (2006) Merging occupancy grid maps from multiple robots. Proc IEEE 94(7):1384–1397CrossRefGoogle Scholar
  2. 2.
    Blanco JL, González-Jiménez J, Fernández-Madrigal JA (2013) A robust, multi-hypothesis approach to matching occupancy grid maps. Robotica 31(5):687–701CrossRefGoogle Scholar
  3. 3.
    Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73CrossRefGoogle Scholar
  4. 4.
    Carlone L, Ng MK, Du J, Bona B, Indri M (2011) Simultaneous localization and mapping using rao-blackwellized particle filters in multi robot systems. J Intell Robot Syst 63(2):283–307CrossRefGoogle Scholar
  5. 5.
    Carpin S (2008) Fast and accurate map merging for multi-robot systems. Auton Robot 25(3):305–316CrossRefGoogle Scholar
  6. 6.
    Carpin S, Birk A, Jucikas V (2005) On map merging. Robot Auton Syst 53(1):1–14CrossRefGoogle Scholar
  7. 7.
    Chetverikov D, Stepanov D, Krsek P (2005) Robust euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm. Image Vis Comput 23 (3):299–309CrossRefGoogle Scholar
  8. 8.
    Choudhary S, Carlone L, Nieto C, Rogers J, Liu Z, Christensen HI, Dellaert F (2016) Multi robot object-based slam. In: International symposium on experimental robotics. Springer, pp 729–741Google Scholar
  9. 9.
    Eliazar AI, Parr R (2006) Hierarchical linear/constant time slam using particle filters for dense maps. In: Advances in neural information processing systems, pp 339–346Google Scholar
  10. 10.
    Fischler MA, Bolles RC (1987) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in computer vision. Elsevier, pp 726–740Google Scholar
  11. 11.
    Huber DF, Hebert M (2003) Fully automatic registration of multiple 3d data sets. Image Vis Comput 21(7):637–650CrossRefGoogle Scholar
  12. 12.
    Jiménez AC, García-díaz V, Bolaños S (2018) A decentralized framework for multi-agent robotic systems. Sensors 18(2):417CrossRefGoogle Scholar
  13. 13.
    Lazaro MT, Paz LM, Pinies P, Castellanos JA, Grisetti G (2013) Multi-robot slam using condensed measurements. In: International conference on intelligent robots and systems (IROS). IEEE, pp 1069–1076Google Scholar
  14. 14.
    Lee C, Yu SE, Kim D (2017) Landmark-based homing navigation using omnidirectional depth information. Sensors 17(8):1928CrossRefGoogle Scholar
  15. 15.
    Lei H, Jiang G, Quan L (2017) Fast descriptors and correspondence propagation for robust global point cloud registration. IEEE Trans Image Process 26(8):3614–3623MathSciNetGoogle Scholar
  16. 16.
    Li H, Tsukada M, Nashashibi F, Parent M (2014) Multivehicle cooperative local mapping: a methodology based on occupancy grid map merging. IEEE Trans Intell Transp Syst 15(5):2089–2100CrossRefGoogle Scholar
  17. 17.
    Lin HY, Yao CW, Cheng KS, Tran VL (2018) Topological map construction and scene recognition for vehicle localization. Auton Robot 42(1):65–81CrossRefGoogle Scholar
  18. 18.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110MathSciNetCrossRefGoogle Scholar
  19. 19.
    Lu H, Li Y, Mu S, Wang D, Kim H, Serikawa S (2017) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things JGoogle Scholar
  20. 20.
    Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Li J, Serikawa S (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and computation: practice and experience 29(6)Google Scholar
  21. 21.
    Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications 23(2):368–375CrossRefGoogle Scholar
  22. 22.
    Lu H, Li Y, Uemura T, Kim H, Serikawa S (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur Gener Comput SystGoogle Scholar
  23. 23.
    Ma L, Zhu J, Zhu L, Du S, Cui J (2016) Merging grid maps of different resolutions by scaling registration. Robotica 34(11):2516–2531CrossRefGoogle Scholar
  24. 24.
    Phillips JM, Liu R, Tomasi C (2007) Outlier robust icp for minimizing fractional rmsd. In: Sixth international conference on 3-d digital imaging and modeling. IEEE, pp 427–434Google Scholar
  25. 25.
    Saeedi S, Paull L, Trentini M, Seto M, Li H (2014) Map merging for multiple robots using hough peak matching. Robot Auton Syst 62(10):1408–1424CrossRefGoogle Scholar
  26. 26.
    Saeedi S, Trentini M, Seto M, Li H (2016) Multiple-robot simultaneous localization and mapping: a review. J Field Rob 33(1):3–46CrossRefGoogle Scholar
  27. 27.
    Senanayake R, Ramos F (2018) Building continuous occupancy maps with moving robots. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, New Orleans, Louisiana, USA, FebruaryGoogle Scholar
  28. 28.
    Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50CrossRefGoogle Scholar
  29. 29.
    Vallicrosa G, Ridao P (2018) H-slam: Rao-blackwellized particle filter slam using hilbert maps. Sensors 18(5)Google Scholar
  30. 30.
    Xu S, Zhu J, Li Y, Wang J, Lu H (2018) Effective scaling registration approach by imposing emphasis on scale factor. Electron Lett 54(7):422–424CrossRefGoogle Scholar
  31. 31.
    Xu X, He L, Lu H, Gao L, Ji Y (2018) Deep adversarial metric learning for cross-modal retrieval. World Wide Web :1–16Google Scholar
  32. 32.
    Yuan X, Martínez JF, Eckert M, López-Santidrián L (2016) An improved otsu threshold segmentation method for underwater simultaneous localization and mapping-based navigation. Sensors 16(7):1148CrossRefGoogle Scholar
  33. 33.
    Zhu J, Du S, Ma L, Yuan Z, Zhang Q (2013) Merging grid maps via point set registration. Int J Robot Autom 28(2)Google Scholar
  34. 34.
    Zhu J, Li Z, Du S, Ma L, Zhang T (2014) Surface reconstruction via efficient and accurate registration of multiview range scans. Opt Eng 53(10):102104CrossRefGoogle Scholar
  35. 35.
    Zhu J, Zhu L, Li Z, Li C, Cui J (2016) Automatic multi-view registration of unordered range scans without feature extraction. Neurocomputing 171:1444–1453CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zutao Jiang
    • 1
  • Jihua Zhu
    • 1
  • Congcong Jin
    • 1
  • Siyu Xu
    • 1
  • Yiqiong Zhou
    • 1
  • Shanmin Pang
    • 1
  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anPeople’s Republic of China

Personalised recommendations