Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

2. Background

verfasst von : Emilio Garcia-Fidalgo, Alberto Ortiz

Erschienen in: Methods for Appearance-based Loop Closure Detection

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This chapter is intended to provide the reader with a general overview of the most important concepts and terms needed to understand the rest of the book. Main concepts are briefly introduced, making use of examples as they are needed for illustration purposes. More precisely, in the first section, we consider the concept of topological map and define it in a formal way, as well as discuss its main advantages and disadvantages in front of metric approaches. Next, we deal with appearance-based loop closure detection and the factors that more affect the performance of the underlying algorithms.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Introduction
Nächstes Kapitel Literature Review
Literatur
1.
Busquets, D.: A multi-agent approach to qualitative navigation in robotics. Ph.D. thesis, Universitat Politècnica de Catalunya (2003)
2.
Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics. Cambridge University Press (2010)
3.
Korrapati, H.: Loop closure for topological mapping and navigation with omnidirectional images. Ph.D. thesis, Université Blaise Pascal-Clermont-Ferrand II (2013)
4.
Wu, J., Rehg, J.M.: CENTRIST: a visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1489–1501 (2011)CrossRef
5.
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. ACM Int. Conf. Image Video Ret. 401–408 (2007)
6.
Fazl-Ersi, E., Tsotsos, J.K.: Histogram of oriented uniform patterns for robust place recognition and categorization. Int. J. Rob. Res. 31(4), 468–483 (2012)CrossRef
7.
Zhou, L., Zhou, Z., Hu, D.: Scene classification using a multi-resolution bag-of-features model. Patt. Recog. 46(1), 424–433 (2013)CrossRef
8.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Conf. Comput. Vision Pattern Recog. 886–893 (2005)
9.
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)
10.
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)CrossRef
11.
Ulrich, I., Nourbakhsh, I.: Appearance-based place recognition for topological localization. IEEE Int. Conf. Robot. Autom. 2, 1023–1029 (2000)
12.
Kosecka, J., Zhou, L., Barber, P., Duric, Z.: Qualitative image based localization in indoors environments. In: IEEE Conf. Comput. Vision Pattern Recog. 2, pp. II–3–II–8 (2003)
13.
Bradley, D., Patel, R., Vandapel, N., Thayer, S.: Real-time image-based topological localization in large outdoor environments. IEEE/RSJ Int. Conf. Intell. Robots Syst. 3670–3677 (2005)
14.
Weiss, C., Masselli, A.: Fast outdoor robot localization using integral invariants. IEEE Int. Conf. Comput. Vision, 1–10 (2007)
15.
Wang, J., Zha, H., Cipolla, R.: Efficient topological localization using orientation adjacency coherence histograms. Int. Conf. Pattern Recog. 271–274 (2006)
16.
Pronobis, A., Caputo, B., Jensfelt, P., Christensen, H.: A discriminative approach to robust visual place recognition. In: IEEE/RSJ Int. Conf. Intell. Robots Syst. 3829–3836 (2006)
17.
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)CrossRefMATH
18.
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)
19.
Sunderhauf, N., Protzel, P.: BRIEF-gist - closing the loop by simple means. IEEE/RSJ Int. Conf. Intell. Robots Syst. 1234–1241 (2011)
20.
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)
21.
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)
22.
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. Robots Syst. 116–121 (2009)
23.
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)
24.
Menegatti, E., Maeda, T., Ishiguro, H.: Image-based memory for robot navigation using properties of omnidirectional images. Rob. Auton. Syst. 47(4), 251–267 (2004)CrossRef
25.
Menegatti, E., Zoccarato, M., Pagello, E., Ishiguro, H.: Image-based monte carlo localisation with omnidirectional images. Rob. Auton. Syst. 48(1), 17–30 (2004)CrossRef
26.
Prasser, D., Wyeth, G.: Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping. IEEE Int. Conf. Robot. Autom. 1, 1291–1296 (2003)
27.
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)CrossRef
28.
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)
29.
Lui, W.L.D., Jarvis, R.: A pure vision-based approach to topological slam. IEEE/RSJ Int. Conf. Intell. Robots Syst. 3784–3791 (2010)
30.
Lui, W.L.D., Jarvis, R.: A pure vision-based topological slam system. Int. J. Robt. Res. 31(4), 403–428 (2012)CrossRef
31.
Badino, H., Huber, D., Kanade, T.: Real-time topometric localization. IEEE Int. Conf. Robot. Autom. 1635–1642 (2012)
32.
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)
33.
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)CrossRef
34.
Tuytelaars, T., Mikolajczyk, K.: Local Invariant Feature Detectors: A Survey. Found. Trends® Comput. Gr. Vis. 3(3), 177–280 (2007)
35.
Schmidt, A., Kraft, M., Kasinski, A.: An evaluation of image feature detectors and descriptors for robot navigation. ICCVG, Computer Vision and Graphic. Lecture Notes in Computer Science, pp. 251–259. Springer, Berlin (2010)
36.
Miksik, O., Mikolajczyk, K.: Evaluation of local detectors and descriptors for fast feature matching. Int. Conf. Pattern Recog. 2681–2684 (2012)
37.
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRef
38.
Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Alvey Vision Conference, pp. 147–151 (1988)
39.
Shi, J., Tomasi, C.: Good features to track. IEEE Conf. Comput. Vision Pattern Recog. 593–600 (1994)
40.
Smith, S., Brady, M.: SUSAN - a new approach to low level image processing. Int. J. Comput. Vision 23(1), 45–78 (1997)CrossRef
41.
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. Eur. Conf. Comput. Vision, 430–443 (2006)
42.
Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–19 (2010)CrossRef
43.
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or surf. IEEE Int. Conf. Comput. Vision 95, 2564–2571 (2011)
44.
Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 6312, pp. 183–196. Springer, Berlin (2010)
45.
Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary robust invariant scalable keypoints. IEEE Int. Conf. Comput. Vision, 2548–2555 (2011)
46.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRef
47.
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 3951, pp. 404–417. Springer, Berlin (2006)
48.
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)
49.
Konolige, K., Bowman, J., Chen, J., Mihelich, P., Calonder, M., Lepetit, V., Fua, P.: View-based maps. Int. J. Robt. Res. 29(8), 941–957 (2010)CrossRef
50.
Ebrahimi, M., Mayol-Cuevas, W.: SUSurE: Speeded up surround extrema feature detector and descriptor for realtime applications. IEEE Conf. Comput. Vision Pattern Recog. 9–14 (2009)
51.
Alcantarilla, P.F., Bartoli, A., Davison, A.J.: KAZE features. European Conference on Computer Vision, pp. 214–227. Springer, Berlin (2012)
52.
Alcantarilla, P.F., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In: British Machine Vision Conference (BMVC) (2013)
53.
Morel, J.M., Yu, G.: ASIFT: a new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2(2), 438–469 (2009)MathSciNetCrossRefMATH
54.
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference (BMVC), pp. 1–10 (2002)
55.
Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. IEEE Conf. Comput. Vision Pattern Recog. 506–513 (2004)
56.
Andreasson, H., Duckett, T.: Topological localization for mobile robots using omnidirectional vision and local features. IFAC Symp. Intell. Auton, Vehic (2008)
57.
Tola, E., Lepetit, V., Fua, P.: DAISY: an efficient dense descriptor applied to wide baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)CrossRef
58.
Sarfraz, M.S., Hellwich, O.: Head Pose Estimation in Face Recognition Across Pose Scenarios. In: International Conference on Computer Vision Theory and Applications, pp. 235–242 (2008)
59.
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)
60.
Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK : fast retina keypoint. IEEE Conf. Comput. Vision Pattern Recog. 510–517 (2012)
61.
Trzcinski, T., Lepetit, V.: Efficient discriminative projections for compact binary descriptors. European Conference on Computer Vision. Lecture Notes in Computer Science, vol. 7572, pp. 228–242 (2012)
62.
Strecha, C., Bronstein, A.M., Bronstein, M.M., Fua, P.: LDAHash: Improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 34(1) (2012)
63.
Trzcinski, T., Christoudias, C., Fua, P., Lepetit, V.: Boosting binary keypoint descriptors. IEEE Conf. Comput. Vision Pattern Recog. 2874–2881 (2013)
64.
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)CrossRef
65.
Geng, L.C., Jodoin, P.M., Su, S.Z., Li, S.Z.: CBDF: compressed binary discriminative feature. Neurocomputing 184, 43–54 (2015)CrossRef
66.
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: International Conference on Very Large Data Bases, pp. 518–529 (1999)
67.
Silpa-Anan, C., Hartley, R.: Optimised kd-trees for fast image descriptor matching. IEEE Conf. Comput. Vision Pattern Recog. 1–8 (2008)
68.
Sivic, J., Zisserman, A.: video google: a text retrieval approach to object matching in videos. IEEE Int. Conf. Comput. Vision, 1470–1477 (2003)
69.
Tsai, C.F.: Bag-of-words representation in image annotation: a review. ISRN Artif. Intell. 2012 (2012)
Metadaten
Titel
Background
verfasst von
Emilio Garcia-Fidalgo
Alberto Ortiz
Copyright-Jahr
2018
Buch
Methods for Appearance-based Loop Closure Detection

Print ISBN: 978-3-319-75992-0

Electronic ISBN: 978-3-319-75993-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75993-7_2

Neuer Inhalt

    Bildnachweise