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Vision-based navigation with efficient scene recognition

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Abstract

In this paper, we propose an efficient feature matching method for scene recognition and global localization. The proposed method enables mobile robots to autonomously navigate through the dynamic environment where the robot frequently encounters visual occlusion and kidnapping. For this purpose, we present a scale optimization method to enhance the matching performance with the combination of the FAST detector and integral image-based SIFT descriptors that are computationally efficient. The scale optimization method is required because the FAST detector does not provide scale information to compute descriptors for matching. We evaluate the performance of feature matching using various indoor image sequences and demonstrate the robustness of our navigation system under various conditions.

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References

  1. 1

    Thrun S (1998) Learning metric-topological maps for indoor mobile robot navigation. Artif Intell 99: 21–71

  2. 2

    Dissanayake G, Newman P, Clark S, Durrant-Whyte F, Csorba M (2001) A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans Robot Autom 17: 229–241

  3. 3

    Se S, Lowe D, Little J (2002) Mobile robot localization and mapping with uncertainty using scale-invariant visual landmakrs. Int J Robot Res 21: 735–758

  4. 4

    Lee K, Doh N, Chung WK (2010) An exploration strategy using sonar sensors in corridor environments. J Intell Serv Robot 3: 89–98

  5. 5

    Chang Y, Yamamoto Y (2009) Path planning of wheeled mobile robot with simultaneous free space locating capability. J Intell Serv Robot 2: 9–22

  6. 6

    Ŝegvić S, Remazeilles A, Diosi A, Chaumette F (2007) Large scale vision-based navigation without an accurate global reconstruction. In: IEEE conference on computer vision and pattern recognition

  7. 7

    Kim J, Bok Y, Kewon I (2008) Robust vision-based navigation against environment changes. In: IEEE/RSJ international conference on intelligent robots and systems

  8. 8

    Kim J, Kweon I (2010) Vision-based navigation with pose recovery under visual occlusion and kidnapping. In: IEEE international conference on robotics and automation

  9. 9

    Furgale P, Barfoot T (2010) Visual teach and repeat for long-range rover autonomy. J Field Robot 27: 534–560

  10. 10

    Yagi Y, Imai K, Tsuji K, Yachida M (2006) Iconic memory-based omnidirectional route panorama navigation. IEEE Trans Pattern Anal Mach Intell 27: 78–87

  11. 11

    Gaspar J, Winters N, Santos-Victor J (2000) Vision-based navigation and environmental representations with an omni-directional camera. IEEE Trans Robot Autom 16: 890–898

  12. 12

    Chen Z, Birchfield S (2006) Qualitative vision-based mobile robot navigation. In: IEEE international conference on robotics and automation

  13. 13

    Booij O, Terwijn B, Zovkovic Z, Kröse B (2007) Navigation using an appearance based topological map. In: IEEE international conference on robotics and automation

  14. 14

    Faundorfer F, Engles C, Nistér D (2007) Topological mapping, localization and navigation using image collections. In: IEEE/RSJ international conference on intelligent robots and systems

  15. 15

    Wolf D, Sukhatme G (2005) Mobile robot simultaneous localization and mapping in dynamic environments. Auton Robot 19: 53–65

  16. 16

    Wang C, Thorpe C, Thrun S (2003) Online simultaneous localization and mapping with detection and tracking of moving objects. In: IEEE international conference on robotics and automation

  17. 17

    Chekhlov D, Pupilli M, Mayol W, Calway A (2007) Robust real-time visual SLAM using scale prediction and exemplar based feature description. In: IEEE international conference on computer vision and pattern recognition

  18. 18

    Williams B, Klein G, Reid I (2007) Real-time SLAM relocalisation. In: IEEE international conference on computer vision

  19. 19

    Lee J, Chung W (2010) Robust mobile robot localization in highly non-static environments. Auton Robot 29: 1–16

  20. 20

    Laaksonen J, Kyrki V (2008) Localization in ambiguous environments using multiple weak cues. J Intell Serv Robot 1: 281–288

  21. 21

    Lowe D (2004) Distinctive image features from scale invariant keypoints. Int J Comput Vis 60: 91–110

  22. 22

    Bay H, Tuytelaars T, Gool L (2006) SURF: speeded up robust features. In: European conference on computer vision

  23. 23

    Rosten E, Porter R, Drummond T (2006) Machine learning for high-speed corner detection. In: Eureopean conference on computer vision

  24. 24

    Nistér D, Stewenius H (2006) Scalable recognition with a vocaburary tree. In: IEEE international conference on computer vision and pattern recognition

  25. 25

    Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22: 1330–1334

  26. 26

    Nistér D, Naroditsky O, Bergen J (2004) Visual odometry. In: IEEE international conference on computer vision and pattern recognition

  27. 27

    Haralick R, Lee C, Ottenberg K, Nölle M (1994) Review and analysis of solutions of the three point perspective pose estimation problem. Int J Comput Vis 13: 331–356

  28. 28

    Fischler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun ACM 24: 381–395

  29. 29

    Triggs B, Mclauchlan P, Hartley R, Fitzgibbon A (2000) Bundle adjustment – a modern synthesis. Vision algorithms: theory and practice. Springer

  30. 30

    Basri R, Rivlin E, Shimshoni I (1990) Visual homing: surfing on the epipoles. Int J Comput Vis 33: 117–137

  31. 31

    Hartley R, Zisserman A (2000) Multiple view geometry in computer vision. Cambridge University Press, London

  32. 32

    Shi J, Tomasi C (1994) Good features to track. In: IEEE conference on computer vision and pattern recognition

  33. 33

    Press W, Teukolsky S, Vetterling W, Flannery B (2002) Numerical recipes in C, 2nd edn. Cambridge University Press, London

  34. 34

    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of sample features. In: IEEE international conference on computer vision and pattern recognition

  35. 35

    Grabner M, Grabner H, Bischof H (2006) Fast approximated SIFT. In: 7th Asian conference on computer vision

  36. 36

    Zhu Q, Avidan S, Yeh M, Cheng K (2006) Fast human detection using a cascade of histograms of oriented gradients. In: IEEE conference on computer vision and pattern recognition

  37. 37

    Duda R, Hart, P, Stork D (2001) Pattern classifcation, 2nd edn. Wiley-Interscience Press, New York, pp 526–528

  38. 38

    Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, Boston

  39. 39

    Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17: 790–799

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Author information

Correspondence to Jungho Kim.

Electronic Supplementary Material

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Kim, J., Park, C. & Kweon, I.S. Vision-based navigation with efficient scene recognition. Intel Serv Robotics 4, 191–202 (2011). https://doi.org/10.1007/s11370-011-0091-x

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Keywords

  • Autonomous navigation
  • Feature matching
  • Scene recognition
  • Global localization