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Holography map for home robot: an object-oriented approach

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Abstract

The environment map plays an important role in robot service, so it should contain not only appearance information about the whole service environment, but also their profoundness. The key contribution of the paper is the presentation of a novel semantic map, namely, a holography map composed of robot, family persons, operable items, local environments, as well as locations and path sections for home service robot cognizing its surroundings and providing services. Inspired by the object-oriented approach, the holography map is divided into three hierarchies of item-room-home and in detail 13 classes of objects. The design and storage of the object-oriented holography map are described comprehensively, and construction of the map is introduced. The execution of robot service based on the object-oriented holography map is discussed briefly. Experiments on real service robot demonstrate that the object-oriented holography map is nearer to human thinking and applicable to indoor robot service tasks.

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References

  1. Rodriguez LD, Matia F, Galan R (2006) Building geometric feature based maps for indoor service robots. Robotics Auton Syst 54(7): 546–558

    Article  Google Scholar 

  2. Choset H, Nagatani K (2001) Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization. IEEE Trans Robotics Automation 17(2): 125–137

    Article  Google Scholar 

  3. Dimitri M, Gregory D (2010) Pure topological mapping in mobile robotics. IEEE Trans Robotics 26(6): 1051–1064

    Article  Google Scholar 

  4. Tomatis N, Nourbakhsh I, Siegwart R (2003) Hybrid simultaneous localization and map building: a natural integration of topological and metric. Robotics Auton Syst 44(1): 3–14

    Article  Google Scholar 

  5. Blanco JL, Gonzalez J, Fernandez-Madrigal JA (2009) Subjective local maps for hybrid metric-topological SLAM. Robotics Auton Syst 57(1): 64–74

    Article  Google Scholar 

  6. Wolf DF, Sukhatme GS (2008) Semantic mapping using mobile robots. IEEE Trans Robotics 24(2): 245–258

    Article  Google Scholar 

  7. Zender H, Martínez MO, Jensfelt P et al (2008) Conceptual spatial representations for indoor mobile robots. Robotics Auton Syst 56(6): 493–502

    Article  Google Scholar 

  8. Douillard B, Fox D, Ramos F et al (2011) Classification and semantic mapping of urban environments. Int J Robotics Res 30(1): 5–32

    Article  Google Scholar 

  9. Vasudevan S, Gachter S, Nguyen V et al (2007) Cognitive maps for mobile robots: an object based approach. Robotics Auton Syst 55(5): 359–371

    Article  Google Scholar 

  10. Vasudevan S, Siegwart R (2008) Bayesian space conceptualization and place classification for semantic maps in mobile robotics. Robotics Auton Syst 56(6): 522–537

    Article  Google Scholar 

  11. Robert C, Ivan A, Gnther S (2011) Step sequence planning for a biped robot by means of a cylindrical shape model and a high-resolution 2.5D map. Robotics Auton Syst 59(2): 84–100

    Article  Google Scholar 

  12. Daniel MD, Wolfram B (2010) Maximum-likelihood sample-based maps for mobile robots. Robotics Auton Syst 58(2): 133–139

    Article  Google Scholar 

  13. Soonyong P, Soohwan K, Mignon P et al (2009) Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts. Inf Sci 179(24): 4174–4198

    Article  Google Scholar 

  14. Patrick B, Joseph M, Benjamin K (2010) Factoring the mapping problem: mobile robot map-building in the hybrid spatial semantic hierarchy. Int J Robotics Res 29(4): 428–459

    Article  Google Scholar 

  15. Cipriano G, Juan-Antonio FM, Javier G et al (2008) Robot task planning using semantic maps. Robotics Auton Syst 56(11): 955–966

    Article  Google Scholar 

  16. Andreas N, Joachim H (2008) Towards semantic maps for mobile robots. Robotics Auton Syst 56(11): 915–926

    Article  Google Scholar 

  17. Staffan E, Danica K, Patric J (2007) Object detection and mapping for service robot tasks. Robotica 25(2): 175–187

    Google Scholar 

  18. Billings SA, Guo LZ, Wei HL (2006) Identification of coupled map lattice models for spatio-temporal patterns using wavelets. Int J Syst Sci 37(14): 1021–1038

    Article  MathSciNet  MATH  Google Scholar 

  19. Cummins M, Newman P (2008) FAB-MAP: Probabilistic localization and mapping in the space of appearance. Int J Robotics Res 27(6): 647–665

    Article  Google Scholar 

  20. Cummins M, Newman P (2010) Accelerating FAB-MAP with concentration inequalities. IEEE Trans Robotics 26(6): 1042–1050

    Article  Google Scholar 

  21. Lim GH, Suh IH, Suh H (2011) Ontology-based unified robot knowledge for service robots in indoor environments. IEEE Trans Syst Man Cybern Part A Syst Hum 41((3): 492–509

    Article  Google Scholar 

  22. Lim GH, Suh IH (2010) Robust robot knowledge instantiation for intelligent service robots. Intell Serv Robotics 3(2): 115–123

    Article  Google Scholar 

  23. Semenov VA, Dragalov KV, Ilyin DV et al (2010) On complementary principles of object-oriented constraint programming. Program Comput Softw 36(5): 264–275

    Article  Google Scholar 

  24. Taher N, Reda A, Mick JR (2009) Two-way mapping between object-oriented databases and XML. Informatica 33(3): 297–308

    MathSciNet  MATH  Google Scholar 

  25. Wu PL, Kong LF, Li HT (2010) Object simultaneous recognition and pose estimation for service robot. J Eng Graph 5: 81–88

    Google Scholar 

  26. Oracle@ Spatial. User’s guide and reference 10g Release 2 (10.2). Spatial data types and metadata 5–11

Download references

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Authors and Affiliations

  1. College of Information Science and Engineering in Yanshan University, 438, Hebei Avenue, Qinhuangdao, 066004, Hebei, People’s Republic of China

    Peiliang Wu, Lingfu Kong & Shengnan Gao

Authors
  1. Peiliang Wu
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  2. Lingfu Kong
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  3. Shengnan Gao
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Corresponding author

Correspondence to Peiliang Wu.

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Wu, P., Kong, L. & Gao, S. Holography map for home robot: an object-oriented approach. Intel Serv Robotics 5, 147–157 (2012). https://doi.org/10.1007/s11370-012-0109-z

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  • DOI: https://doi.org/10.1007/s11370-012-0109-z

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