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Literature Review

  • Emilio Garcia-Fidalgo
  • Alberto Ortiz
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 122)

Abstract

This chapter reviews the main approaches published during the last years with regard to topological mapping and localization by visual means. We classify the different solutions according to the method used to visually describe an image, given the fact that the quality of the resulting map strongly relies on this aspect. Three fundamental categories are distinguished: approaches based on global descriptors, approaches based on local features and approaches based on Bag-Of-Words (BoW) schemes. We also consider different combinations of these methods.

References

  1. 1.
    Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. J. Intell. Robot. Syst. 53(3), 263–296 (2008)CrossRefGoogle Scholar
  2. 2.
    Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendón-Mancha, J.M.: Visual simultaneous localization and mapping: a survey. Artif. Intell. Rev. 43(1), 55–81 (2015)CrossRefGoogle Scholar
  3. 3.
    Olson, E., Leonard, J., Teller, S.: Fast iterative alignment of pose graphs with poor initial estimates. IEEE Int. Conf. Robot. Autom. 2262–2269 (2006)Google Scholar
  4. 4.
    Frese, U., Schroder, L.: Closing a million-landmarks loop. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 5032–5039 (2006)Google Scholar
  5. 5.
    Dellaert, F., Kaess, M.: Square root sam: simultaneous localization and mapping via square root information smoothing. Int. J. Robot. Res. 25(12), 1181–1203 (2006)CrossRefMATHGoogle Scholar
  6. 6.
    Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: incremental smoothing and mapping. IEEE Trans. Robot. 24(6), 1365–1378 (2008)CrossRefGoogle Scholar
  7. 7.
    Grisetti, G., Stachniss, C., Burgard, W.: Nonlinear constraint network optimization for efficient map learning. IEEE Trans. Intell. Transp. Syst. 10(3), 428–439 (2009)CrossRefGoogle Scholar
  8. 8.
    Konolige, K., Grisetti, G., Kummerle, R., Burgard, W., Limketkai, B., Vincent, R.: Efficient sparse pose adjustment for 2d mapping. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 22–29 (2010)Google Scholar
  9. 9.
    Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J., Dellaert, F.: iSAM2: incremental smoothing and mapping with fluid relinearization and incremental variable reordering. IEEE Int. Conf. Robot. Autom. 3281–3288 (2011)Google Scholar
  10. 10.
    Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. IEEE Int. Conf. Robot. Autom. 3607–3613 (2011)Google Scholar
  11. 11.
    Wu, J., Christensen, H., Rehg, J.: Visual place categorization: problem, dataset, and algorithm. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 4763–4770 (2009)Google Scholar
  12. 12.
    Winters, N., Gaspar, J., Lacey, G., Santos-Victor, J.: Omni-directional vision for robot navigation. In: IEEE workshop on omnidirectional vision, pp. 21–28 (2000)Google Scholar
  13. 13.
    Gaspar, J., Winters, N., Santos-Victor, J.: Vision-based navigation and environmental representations with an omnidirectional camera. IEEE Trans. Robot. Autom. 16(6), 890–898 (2000)CrossRefGoogle Scholar
  14. 14.
    Ulrich, I., Nourbakhsh, I.: Appearance-based place recognition for topological localization. IEEE Int. Conf. Robot. Autom. 2, 1023–1029 (2000)Google Scholar
  15. 15.
    Werner, F., Maire, F., Sitte, J.: Topological slam using fast vision techniques. Advances in Robotics, pp. 187–196. Springer, Berlin (2009)Google Scholar
  16. 16.
    Kosecka, J., Zhou, L., Barber, P., Duric, Z.: Qualitative image based localization in indoors environments. IEEE Conf. Comput. Vis. Pattern Recog. 2, II-3–II-8 (2003)Google Scholar
  17. 17.
    Bradley, D., Patel, R., Vandapel, N., Thayer, S.: Real-time image-based topological localization in large outdoor environments. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3670–3677 (2005)Google Scholar
  18. 18.
    Weiss, C., Masselli, A.: Fast outdoor robot localization using integral invariants. IEEE Int. Conf. Comput. Vis. 1–10 (2007)Google Scholar
  19. 19.
    Wang, J., Zha, H., Cipolla, R.: Efficient topological localization using orientation adjacency coherence histograms. Int. Conf. Pattern Recog. 271–274 (2006)Google Scholar
  20. 20.
    Pronobis, A., Caputo, B., Jensfelt, P., Christensen, H.: A discriminative approach to robust visual place recognition. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3829–3836 (2006)Google Scholar
  21. 21.
    Singh, G., Kosecka, J.: Visual loop closing using gist descriptors in manhattan world. In: IEEE Workshop on Omnidirectional Vision, Camera Networks and Non-classical Camera (2010)Google Scholar
  22. 22.
    Murillo, A.C., Campos, P., Kosecka, J., Guerrero, J.: Gist vocabularies in omnidirectional images for appearance based mapping and localization. In: Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras (RSS) (2010)Google Scholar
  23. 23.
    Rituerto, A., Murillo, A.C., Guerrero, J.: Semantic labeling for indoor topological mapping using a wearable catadioptric system. Robot. Auton. Syst. 62, 685–695 (2013)CrossRefGoogle Scholar
  24. 24.
    Sunderhauf, N., Protzel, P.: BRIEF-gist - closing the loop by simple means. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 1234–1241 (2011)Google Scholar
  25. 25.
    Arroyo, R., Alcantarilla, P.F., Bergasa, L.M., Yebes, J., Gamez, S.: Bidirectional loop closure detection on panoramas for visual navigation. Intell. Vehic. Symp. 1378–1383 (2014)Google Scholar
  26. 26.
    Arroyo, R., Alcantarilla, P.F., Bergasa, L.M., Yebes, J.J., Bronte, S.: fast and effective visual place recognition using binary codes and disparity information. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (2014)Google Scholar
  27. 27.
    Liu, Y., Zhang, H.: Visual loop closure detection with a compact image descriptor. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 1051–1056 (2012)Google Scholar
  28. 28.
    Chapoulie, A., Rives, P., Filliat, D.: Topological segmentation of indoors/outdoors sequences of spherical views. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 4288–4295 (2012)Google Scholar
  29. 29.
    Chapoulie, A., Rives, P., Filliat, D.: Appearance-based segmentation of indoors and outdoors sequences of spherical views. IEEE Int. Conf. Robot. Autom. 1946–1951 (2013)Google Scholar
  30. 30.
    Lamon, P., Nourbakhsh, I., Jensen, B., Siegwart, R.: Deriving and matching image fingerprint sequences for mobile robot localization. IEEE Int. Conf. Robot. Autom. 2, 1609–1614 (2001)Google Scholar
  31. 31.
    Tapus, A., Tomatis, N., Siegwart, R.: Topological global localization and mapping with fingerprints and uncertainty. In: International symposium on experimental robotics, pp. 18–21 (2004)Google Scholar
  32. 32.
    Tapus, A., Siegwart, R.: Incremental robot mapping with fingerprints of places. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 2429–2434 (2005)Google Scholar
  33. 33.
    Liu, M., Scaramuzza, D., Pradalier, C., Siegwart, R., Chen, Q.: Scene recognition with omnidirectional vision for topological map using lightweight adaptive descriptors. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 116–121 (2009)Google Scholar
  34. 34.
    Liu, M., Siegwart, R.: DP-FACT: Towards topological mapping and scene recognition with color for omnidirectional camera. IEEE Int. Conf. Robot. Autom. 3503–3508 (2012)Google Scholar
  35. 35.
    Menegatti, E., Maeda, T., Ishiguro, H.: Image-based memory for robot navigation using properties of omnidirectional images. Robot. Auton. Syst. 47(4), 251–267 (2004)CrossRefGoogle Scholar
  36. 36.
    Menegatti, E., Zoccarato, M., Pagello, E., Ishiguro, H.: Image-based monte carlo localisation with omnidirectional images. Robot. Auton. Syst. 48(1), 17–30 (2004)CrossRefGoogle Scholar
  37. 37.
    Payá, L., Fernández, L., Gil, A., Reinoso, O.: Map building and monte carlo localization using global appearance of omnidirectional images. Sensors 10(12), 11468–11497 (2010)CrossRefGoogle Scholar
  38. 38.
    Ranganathan, A., Menegatti, E., Dellaert, F.: Bayesian inference in the space of topological maps. IEEE Trans. Robot. 22(1), 92–107 (2006)CrossRefGoogle Scholar
  39. 39.
    Milford, M., Wyeth, G., Prasser, D.: RatSLAM: A hippocampal model for simultaneous localization and mapping. In: IEEE Int. Conf. Robot. Autom. 403–408 (2004)Google Scholar
  40. 40.
    Prasser, D., Milford, M., Wyeth, G.: Outdoor simultaneous localisation and mapping using RatSLAM. FSR 143–154 (2005)Google Scholar
  41. 41.
    Milford, M., Wyeth, G.: Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Trans. Robot. 24(5), 1038–1053 (2008)CrossRefGoogle Scholar
  42. 42.
    Glover, A., Maddern, W., Milford, M., Wyeth, G.: FAB-MAP + RatSLAM: appearance-based slam for multiple times of day. IEEE Int. Conf. Robot. Autom. 3507–3512 (2010)Google Scholar
  43. 43.
    Lui, W.L.D., Jarvis, R.: A pure vision-based approach to topological SLAM. IEEE/RSJ Int. Conf. Intell. Robots Syst. 784–3791 (2010)Google Scholar
  44. 44.
    Lui, W.L.D., Jarvis, R.: A pure vision-based topological SLAM system. Int. J. Robot. Res. 31(4), 403–428 (2012)CrossRefGoogle Scholar
  45. 45.
    Badino, H., Huber, D., Kanade, T.: Real-time topometric localization. IEEE Int. Conf. Robot. Autom. 1635–1642 (2012)Google Scholar
  46. 46.
    Xu, D., Badino, H., Huber, D.: Topometric localization on a road network. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (2014)Google Scholar
  47. 47.
    Lategahn, H., Beck, J., Kitt, B., Stiller, C.: How to learn an illumination robust image feature for place recognition. Intell. Vehic. Symp. 285–291 (2013)Google Scholar
  48. 48.
    Nourani-Vatani, N., Borges, P., Roberts, J., Srinivasan, M.: On the use of optical flow for scene change detection and description. J. Intell. Robot. Syst. 74(3), 817–846 (2014)CrossRefGoogle Scholar
  49. 49.
    Milford, M., Wyeth, G.: SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights. IEEE Int. Conf. Robot. Autom. 1643–1649 (2012)Google Scholar
  50. 50.
    Milford, M.: Visual route recognition with a handful of bits. Robot. Sci. Syst. (2013)Google Scholar
  51. 51.
    Milford, M.: Vision-based place recognition: how low can you go? Int. J. Robot. Res. 32(7), 766–789 (2013)CrossRefGoogle Scholar
  52. 52.
    Pepperell, E., Corke, P., Milford, M.: All-environment visual place recognition with SMART. IEEE Int. Conf. Robot. Autom. 1612–1618 (2014)Google Scholar
  53. 53.
    Wu, J., Zhang, H., Guan, Y.: An efficient visual loop closure detection method in a map of 20 million key locations. IEEE Int. Conf. Robot. Autom. 861–866 (2014)Google Scholar
  54. 54.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefMATHGoogle Scholar
  55. 55.
    Siagian, C., Itti, L.: Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 300–12 (2007)CrossRefGoogle Scholar
  56. 56.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 6314, pp. 778–792. Springer, Berlin (2010)Google Scholar
  57. 57.
    Yang, X., Cheng, K.T.: Local difference binary for ultrafast and distinctive feature description. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 188–94 (2014)CrossRefGoogle Scholar
  58. 58.
    Prasser, D., Wyeth, G.: Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping. IEEE Int. Conf. Robot. Autom. 1, 1291–1296 (2003)Google Scholar
  59. 59.
    Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: center surround extremas for realtime feature detection and matching. European Conference on Computer Vision, vol. 5305, pp. 102–115. Springer, Berlin (2008)Google Scholar
  60. 60.
    Kosecka, J., Yang, X.: Location recognition and global localization based on scale-invariant keypoints. In: Workshop on Statistical Learning in Computer Vision (ECCV) (2004)Google Scholar
  61. 61.
    Kosecka, J., Li, F.: Vision based topological markov localization. IEEE Int. Conf. Robot. Autom. 2, 1481–1486 (2004)Google Scholar
  62. 62.
    Li, F., Kosecka, J.: Probabilistic location recognition using reduced feature set. IEEE Int. Conf. Robot. Autom. 405–3410 (2006)Google Scholar
  63. 63.
    Zhang, H.: BoRF: loop-closure detection with scale invariant visual features. IEEE Int. Conf. Robot. Autom. 3125–3130 (2011)Google Scholar
  64. 64.
    Zhang, H.: Indexing visual features: real-time loop closure detection using a tree structure. IEEE Int. Conf. Robot. Autom. 3613–3618 (2012)Google Scholar
  65. 65.
    Rybski, P., Zacharias, F., Lett, J.F., Masoud, O., Gini, M., Papanikolopoulos, N.: Using visual features to build topological maps of indoor environments. IEEE Int. Conf. Robot. Autom. 1, 850–855 (2003)Google Scholar
  66. 66.
    He, X., Zemel, R., Mnih, V.: Topological map learning from outdoor image sequences. J. Field Robot. 23(11–12), 1091–1104 (2006)CrossRefGoogle Scholar
  67. 67.
    Sabatta, D.G.: vision-based topological map building and localisation using persistent features. In: Robotics and mechatronics symposium, pp. 1–6 (2008)Google Scholar
  68. 68.
    Johns, E., Yang, G.Z.: Global localization in a dense continuous topological map. IEEE Int. Conf. Robot. Autom. 1032–1037 (2011)Google Scholar
  69. 69.
    Kawewong, A., Tangruamsub, S., Hasegawa, O.: Position-invariant robust features for long-term recognition of dynamic outdoor scenes. IEICET. Inf. Syst. E93-D(9), 2587–2601 (2010)Google Scholar
  70. 70.
    Kawewong, A., Tongprasit, N., Tangruamsub, S., Hasegawa, O.: Online and incremental appearance-based SLAM in highly dynamic environments. Int. J. Robot. Res. 30(1), 33–55 (2011)CrossRefGoogle Scholar
  71. 71.
    Tongprasit, N., Kawewong, A., Hasegawa, O.: PIRF-Nav 2: speeded-up online and incremental appearance-based slam in an indoor environment. In: IEEE Workshop on Applications of Computer Vision, pp. 145–152 (2011)Google Scholar
  72. 72.
    Morioka, H., Yi, S., Hasegawa, O.: Vision-based mobile robot’s slam and navigation in crowded environments. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3998–4005 (2011)Google Scholar
  73. 73.
    Andreasson, H., Duckett, T.: Topological localization for mobile robots using omnidirectional vision and local features. IFAC Symp. Intell. Auton. Vehic. (2008)Google Scholar
  74. 74.
    Valgren, C., Lilienthal, A., Duckett, T.: Incremental topological mapping using omnidirectional vision. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3441–3447 (2006)Google Scholar
  75. 75.
    Valgren, C., Duckett, T., Lilienthal, A.: Incremental spectral clustering and its application to topological mapping. IEEE Int. Conf. Robot. Autom. 10–14 (2007)Google Scholar
  76. 76.
    Valgren, C., Lilienthal, A.: SIFT, SURF and seasons: long-term outdoor localization using local features. Eur. Conf. Mob. Rob. 128, 1–6 (2007)Google Scholar
  77. 77.
    Ascani, A., Frontoni, E., Mancini, A., Zingaretti, P.: Feature group matching for appearance-based localization. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3933–3938 (2008)Google Scholar
  78. 78.
    Anati, R., Daniilidis, K.: constructing topological maps using markov random fields and loop-closure detection. In: Advances in Neural Information Processing Systems, pp. 37–45 (2009)Google Scholar
  79. 79.
    Zivkovic, Z., Bakker, B., Krose, B.: Hierarchical map building using visual landmarks and geometric constraints. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 2480–2485 (2005)Google Scholar
  80. 80.
    Booij, O., Terwijn, B., Zivkovic, Z., Krose, B.: Navigation using an appearance based topological map. IEEE Int. Conf. Robot. Autom. 3927–3932 (2007)Google Scholar
  81. 81.
    Booij, O., Zivkovic, Z., Krose, B.: Efficient data association for view based slam using connected dominating sets. Robot. Auton. Syst. 57(12), 1225–1234 (2009)CrossRefGoogle Scholar
  82. 82.
    Dayoub, F., Cielniak, G., Duckett, T.: A sparse hybrid map for vision-guided mobile robots. In: Eur. Conf. Mob. Robot. 213–218 (2011)Google Scholar
  83. 83.
    Blanco, J.L., Fernandez-Madrigal, J.A., Gonzalez, J.: Towards a unified bayesian approach to hybrid metric-topological SLAM. IEEE Trans. Robot. 24(2), 259–270 (2008)CrossRefGoogle Scholar
  84. 84.
    Blanco, J.L., Gonzalez, J., Fernandez-Madrigal, J.A.: Subjective local maps for hybrid metric-topological SLAM. Robot. Auton. Syst. 57(1), 64–74 (2009)CrossRefGoogle Scholar
  85. 85.
    Tully, S., Moon, H., Morales, D., Kantor, G., Choset, H.: Hybrid localization using the hierarchical atlas. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 2857–2864 (2007)Google Scholar
  86. 86.
    Tully, S., Kantor, G., Choset, H., Werner, F.: A multi-hypothesis topological slam approach for loop closing on edge-ordered graphs. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 4943–4948 (2009)Google Scholar
  87. 87.
    Segvic, S., Remazeilles, A., Diosi, A., Chaumette, F.: A mapping and localization framework for scalable appearance-based navigation. Comput. Vis. Image Und. 113(2), 172–187 (2009)CrossRefGoogle Scholar
  88. 88.
    Ramisa, A., Tapus, A., Aldavert, D., Toledo, R.: Lopez de Mantaras, R.: Robust vision-based robot localization using combinations of local feature region detectors. Auton. Robot. 27(4), 373–385 (2009)CrossRefGoogle Scholar
  89. 89.
    Badino, H., Huber, D., Kanade, T.: Visual topometric localization. Intell. Vehic. Symp. 794–799 (2011)Google Scholar
  90. 90.
    Dayoub, F., Duckett, T.: an adaptive appearance-based map for long-term topological localization of mobile robots. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3364 – 3369 (2008)Google Scholar
  91. 91.
    Bacca, B., Salvi, J., Batlle, J., Cufi, X.: Appearance-based mapping and localisation using feature stability histograms. Elect. Lett. 46(16), 1120 (2010)CrossRefGoogle Scholar
  92. 92.
    Bacca, B., Salvi, J., Cufi, X.: Appearance-based mapping and localization for mobile robots using a feature stability histogram. Robot. Auton. Syst. 59(10), 840–857 (2011)CrossRefGoogle Scholar
  93. 93.
    Bacca, B., Salvi, J., Cufi, X.: Long-term mapping and localization using feature stability histograms. Robot. Auton. Syst. 61(12), 1539–1558 (2013)CrossRefGoogle Scholar
  94. 94.
    Romero, A., Cazorla, M.: Topological SLAM using omnidirectional images: merging feature detectors and graph-matching. Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, vol. 6474, pp. 464–475. Springer, Berlin (2010)Google Scholar
  95. 95.
    Romero, A., Cazorla, M.: Topological visual mapping in robotics. Cogn. Process. 13(1), 305–308 (2012)CrossRefGoogle Scholar
  96. 96.
    Majdik, A., Albers-Schoenberg, Y., Scaramuzza, D.: MAV urban localization from google street view data. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3979–3986 (2013)Google Scholar
  97. 97.
    Saedan, M., Lim, C.W., Ang, M.: Appearance-based slam with map loop closing using an omnidirectional camera. IEEE Int. Conf. Adv. Intell. Mech. 1–6 (2007)Google Scholar
  98. 98.
    Kessler, J., König, A., Gross, H.M.: An improved sensor model on appearance based SLAM. Auton. Mob. Syst. 216487, 153–160 (2009)Google Scholar
  99. 99.
    Maohai, L., Han, W., Lining, S., Zesu, C.: Robust omnidirectional mobile robot topological navigation system using omnidirectional vision. Eng. Appl. Artif. Intell. 26(8), 1942–1952 (2013)CrossRefGoogle Scholar
  100. 100.
    Zhang, H., Li, B., Yang, D.: Keyframe detection for appearance-based visual SLAM. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 2071–2076 (2010)Google Scholar
  101. 101.
    Lisien, B., Morales, D., Silver, D., Kantor, G., Rekleitis, I.M., Choset, H.: The hierarchical atlas. IEEE Trans. Robot. 21(3), 473–481 (2005)CrossRefGoogle Scholar
  102. 102.
    Tully, S., Kantor, G., Choset, H.: A unified bayesian framework for global localization and SLAM in hybrid metric/topological maps. Int. J. Robot. Res. 31(3), 271–288 (2012)CrossRefGoogle Scholar
  103. 103.
    Atkinson, R.C., Shiffrin, R.M.: Human memory: a proposed system and its control processes. Psychol. Learn. Motiv. Adv. Res. Theory 2, 89–105 (1968)CrossRefGoogle Scholar
  104. 104.
    Wang, J., Cipolla, R., Zha, H.: Vision-based global localization using a visual vocabulary. IEEE Int. Conf. Robot. Autom. 4230–4235 (2005)Google Scholar
  105. 105.
    Wang, J., Zha, H., Cipolla, R.: Coarse-to-fine vision-based localization by indexing scale-invariant features. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(2), 413–422 (2006)CrossRefGoogle Scholar
  106. 106.
    Fraundorfer, F., Engels, C., Nister, D.: Topological mapping, localization and navigation using image collections. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3872–3877 (2007)Google Scholar
  107. 107.
    Konolige, K., Bowman, J., Chen, J., Mihelich, P., Calonder, M., Lepetit, V., Fua, P.: View-based maps. Int. J. Robot. Res. 29(8), 941–957 (2010)CrossRefGoogle Scholar
  108. 108.
    Cummins, M., Newman, P.: Probabilistic appearance based navigation and loop closing. IEEE Int. Conf. Robot. Autom. 2042–2048 (2007)Google Scholar
  109. 109.
    Cummins, M., Newman, P.: FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27(6), 647–665 (2008)Google Scholar
  110. 110.
    Cummins, M., Newman, P.: Accelerated appearance-only SLAM. IEEE Int. Conf. Robot. Autom. 1828–1833 (2008)Google Scholar
  111. 111.
    Cummins, M., Newman, P.: Accelerating FAB-MAP with concentration inequalities. IEEE Trans. Robot. 26(6), 1042–1050 (2010)CrossRefGoogle Scholar
  112. 112.
    Cummins, M., Newman, P.: Highly scalable appearance-only SLAM - FAB-MAP 2.0. Robot. Sci. Syst. 1–8 (2009)Google Scholar
  113. 113.
    Cummins, M., Newman, P.: Appearance-only SLAM at large scale with FAB-MAP 2.0. Int. J. Robot. Res. 30(9), 1100–1123 (2011)Google Scholar
  114. 114.
    Newman, P., Sibley, G., Smith, M., Cummins, M., Harrison, A., Mei, C., Posner, I., Shade, R., Schroeter, D., Murphy, L., Churchill, W., Cole, D., Reid, I.: Navigating, recognizing and describing urban spaces with vision and lasers. Int. J. Robot. Res. 28(11–12), 1406–1433 (2009)CrossRefGoogle Scholar
  115. 115.
    Maddern, W., Milford, M., Wyeth, G.: Continuous appearance-based trajectory SLAM. IEEE Int. Conf. Robot. Autom. 3595–3600 (2011)Google Scholar
  116. 116.
    Maddern, W., Milford, M., Wyeth, G.: CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory. Int. J. Robot. Res. 31(4), 429–451 (2012)CrossRefGoogle Scholar
  117. 117.
    Maddern, W., Milford, M., Wyeth, G.: Towards persistent indoor appearance-based localization, mapping and navigation using CAT-graph. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 4224–4230 (2012)Google Scholar
  118. 118.
    Paul, R., Newman, P.: FAB-MAP 3D: Topological mapping with spatial and visual appearance. IEEE Int. Conf. Robot. Autom. 2649–2656 (2010)Google Scholar
  119. 119.
    Johns, E., Yang, G.Z.: Feature co-occurrence maps: appearance-based localisation throughout the day. IEEE Int. Conf. Robot. Autom. 3212–3218 (2013)Google Scholar
  120. 120.
    Johns, E., Yang, G.Z.: Dynamic scene models for incremental, long term, appearance based localisation. IEEE Int. Conf. Robot. Autom. 2731–2736 (2013)Google Scholar
  121. 121.
    Galvez-Lopez, D., Tardos, J.: Real-time loop detection with bags of binary words. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 51–58 (2011)Google Scholar
  122. 122.
    Galvez-Lopez, D., Tardos, J.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28(5), 1188–1197 (2012)CrossRefGoogle Scholar
  123. 123.
    Mur-Artal, R., Tardos, J.D.: Fast relocalisation and loop closing in keyframe-based SLAM. IEEE Int. Conf. Robot. Autom. 846–853 (2014)Google Scholar
  124. 124.
    Ranganathan, A., Dellaert, F.: Online probabilistic topological mapping. Int. J. Robot. Res. 30(6), 755–771 (2011)CrossRefGoogle Scholar
  125. 125.
    Cadena, C., Galvez-Lopez, D., Ramos, F., Tardos, J., Neira, J.: Robust place recognition with stereo cameras. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 5182–5189 (2010)Google Scholar
  126. 126.
    Ciarfuglia, T., Costante, G., Valigi, P., Ricci, E.: A discriminative approach for appearance based loop closing. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3837–3843 (2012)Google Scholar
  127. 127.
    Majdik, A., Galvez-Lopez, D., Lazea, G., Castellanos, J.: Adaptive appearance based loop-closing in heterogeneous environments. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 1256–1263 (2011)Google Scholar
  128. 128.
    Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. IEEE Conf. Comput. Vis. Pattern Recog. 1–7 (2007)Google Scholar
  129. 129.
    Achar, S., Jawahar, C., Madhava Krishna, K.: Large scale visual localization in urban environments. IEEE Int. Conf. Robot. Autom. 5642–5648 (2011)Google Scholar
  130. 130.
    Lee, J.H., Zhang, G., Lim, J., Suh, I.H.: Place recognition using straight lines for vision-based SLAM. IEEE Int. Conf. Robot. Autom. 3799–3806 (2013)Google Scholar
  131. 131.
    Filliat, D.: A visual bag of words method for interactive qualitative localization and mapping. IEEE Int. Conf. Robot. Autom. 3921–3926 (2007)Google Scholar
  132. 132.
    Angeli, A., Doncieux, S., Meyer, J.A., Filliat, D.: Real-time visual loop-closure detection. IEEE Int. Conf. Robot. Autom. 1842–1847 (2008)Google Scholar
  133. 133.
    Angeli, A., Filliat, D., Doncieux, S., Meyer, J.A.: A fast and incremental method for loop-closure detection using bags of visual words. IEEE Trans. Robot. 24(5), 1027–1037 (2008)CrossRefGoogle Scholar
  134. 134.
    Angeli, A., Doncieux, S., Meyer, J.A., Filliat, D.: Incremental vision-based topological SLAM. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 1031–1036 (2008)Google Scholar
  135. 135.
    Labbe, M., Michaud, F.: Memory management for real-time appearance-based loop closure detection. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 1271–1276 (2011)Google Scholar
  136. 136.
    Labbe, M., Michaud, F.: Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Trans. Robot. 29(3), 734–745 (2013)CrossRefGoogle Scholar
  137. 137.
    Nicosevici, T., Garcia, R.: On-line visual vocabularies for robot navigation and mapping. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 205–212 (2009)Google Scholar
  138. 138.
    Nicosevici, T., Garcia, R.: Automatic visual bag-of-words for online robot navigation and mapping. IEEE Trans. Robot. 28(4), 886–898 (2012)CrossRefGoogle Scholar
  139. 139.
    Khan, S., Wollherr, D.: IBuILD: Incremental bag of binary words for appearance-based loop closure detection. IEEE Int. Conf. Robot. Autom. 5441–5447 (2015)Google Scholar
  140. 140.
    Murphy, L., Sibley, G.: Incremental unsupervised topological place discovery. IEEE Int. Conf. Robot. Autom. 1312–1318 (2014)Google Scholar
  141. 141.
    MacTavish, K., Barfoot, T.D.: Towards hierarchical place recognition for long-term autonomy. IEEE Int. Conf. Robot. Autom. (2014)Google Scholar
  142. 142.
    Mohan, M., Galvez-Lopez, D., Monteleoni, C., Sibley, G.: Environment selection and hierarchical place recognition. IEEE Int. Conf. Robot. Autom. (2015)Google Scholar
  143. 143.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. Eur. Conf. Comput. Vis. 1, 1–22 (2004)Google Scholar
  144. 144.
    Li, F.F., Perona, P.: A bayesian hierarchical model for learning natural scene categories. IEEE Conf. Comput. Vis. Pattern Recog. 524–531 (2005)Google Scholar
  145. 145.
    Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. IEEE Int. Conf. Comput. Vis. 1470–1477 (2003)Google Scholar
  146. 146.
    Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. IEEE Conf. Comput. Vis. Pattern Recog. 2, 2161–2168 (2006)Google Scholar
  147. 147.
    Glover, A., Maddern, W., Warren, M., Reid, S., Milford, M., Wyeth, G.: OpenFABMAP: an open source toolbox for appearance-based loop closure detection. IEEE Int. Conf. Robot. Autom. 4730 – 4735 (2012)Google Scholar
  148. 148.
    Ranganathan, A., Dellaert, F.: a rao-blackwellized particle filter for topological mapping. IEEE Int. Conf. Robot. Autom. 810–817 (2006)Google Scholar
  149. 149.
    Latif, Y., Cadena, C., Neira, J.: Realizing, reversing, recovering: incremental robust loop closing over time using the irrr algorithm. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 4211–4217 (2012)Google Scholar
  150. 150.
    Eade, E., Drummond, T.: Unified loop closing and recovery for real time monocular SLAM. British Mach. Vis. Conf. 1–10 (2008)Google Scholar
  151. 151.
    Botterill, T., Mills, S., Green, R.: Bag-of-words-driven, single-camera simultaneous localization and mapping. J. Field Robot. 28(2), 204–226 (2011)CrossRefGoogle Scholar
  152. 152.
    Pradeep, V., Medioni, G., Weiland, J.: Visual loop closing using multi-resolution SIFT Grids in metric-topological SLAM. IEEE Conf. Comput. Vis. Pattern Recog. 1438–1445 (2009)Google Scholar
  153. 153.
    Goedemé, T., Nuttin, M., Tuytelaars, T., Van Gool, L.: Markerless computer vision based localization using automatically generated topological maps. In: European Navigation Conference, pp. 235–243 (2004)Google Scholar
  154. 154.
    Goedemé, T., Nuttin, M., Tuytelaars, T., Van Gool, L.: Omnidirectional vision based topological navigation. Int. J. Comput. Vis. 74(3), 219–236 (2007)CrossRefGoogle Scholar
  155. 155.
    Murillo, A.C., Sagues, C., Guerrero, J.: From omnidirectional images to hierarchical localization. Robot. Auton. Syst. 55(5), 372–382 (2007)CrossRefGoogle Scholar
  156. 156.
    Murillo, A.C., Guerrero, J., Sagues, C.: SURF features for efficient robot localization with omnidirectional images. IEEE Int. Conf. Robot. Autom. 3901–3907 (2007)Google Scholar
  157. 157.
    Wang, J., Yagi, Y.: Efficient Topological Localization Using Global and Local Feature Matching. Int. J. Adv. Robot. Syst. 10(153:2013), – (2013)Google Scholar
  158. 158.
    Weiss, C., Masselli, A., Zell, A.: fast vision-based localization for outdoor robots using a combination of global image features. IFAC Symp. on Intell. Auton. Vehic. 119–124 (2007)Google Scholar
  159. 159.
    Weiss, C., Tamimi, H., Masselli, A., Zell, A.: A hybrid approach for vision-based outdoor robot localization using global and local image features. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 1047–1052 (2007)Google Scholar
  160. 160.
    Siagian, C., Itti, L.: Biologically inspired mobile robot vision localization. IEEE Trans. Robot. 25(4), 861–873 (2009)CrossRefGoogle Scholar
  161. 161.
    Chapoulie, A., Rives, P., Filliat, D.: A spherical representation for efficient visual loop closing. IEEE Int. Conf. Comput. Vis. 335–342 (2011)Google Scholar
  162. 162.
    Wang, M.L., Lin, H.Y.: A hull census transform for scene change detection and recognition towards topological map building. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 548–553 (2010)Google Scholar
  163. 163.
    Lin, H.Y., Lin, Y.H., Yao, J.W.: Scene Change Detection and Topological Map Construction Using Omnidirectional Image Sequences. In: IAPR Int. Conf. Mach. Vision App. 56–60 (2013)Google Scholar
  164. 164.
    Wang, J., Yagi, Y.: Robust location recognition based on efficient feature integration. IEEE Int. Conf. Robot. Biomim. 97–101 (2012)Google Scholar
  165. 165.
    Maohai, L., Lining, S., Qingcheng, H., Zesu, C., Songhao, P.: Robust omnidirectional vision based mobile robot hierarchical localization and autonomous navigation. Inf. Tech. J. 10(1), 29–39 (2011)CrossRefGoogle Scholar
  166. 166.
    Korrapati, H., Uzer, F., Mezouar, Y.: Hierarchical visual mapping with omnidirectional images. IEEE/RSJ Int. Conf. Intell. Robot. Syst. 3684–3690 (2013)Google Scholar
  167. 167.
    Korrapati, H., Mezouar, Y.: Vision-based sparse topological mapping. Robot. Auton. Syst. 62(9), 1259–1270 (2014)CrossRefGoogle Scholar
  168. 168.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Conf. Comput. Vis. Pattern Recog. 2, 2169–2178 (2006)Google Scholar
  169. 169.
    Hou, J., Liu, W.X., E, X., Xia, Q., Qi, N.M.: An experimental study on the universality of visual vocabularies. J. Vis. Commun. Image. Represent. 24(7), 1204–1211 (2013)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of the Balearic IslandsPalma de MallorcaSpain

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