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:
Query-Focused Extractive Video Summarization Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Online Variational Bayesian Motion Averaging

verfasst von : Guillaume Bourmaud

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

In this paper, we propose a novel algorithm dedicated to online motion averaging for large scale problems. To this end, we design a filter that continuously approximates the posterior distribution of the estimated transformations. In order to deal with large scale problems, we associate a variational Bayesian approachwith a relative parametrization of the absolute transformations. Such an association allows our algorithm to simultaneously possess two features that are essential for an algorithm dedicated to large scale online motion averaging: (1) a low computational time, (2) the ability to detect wrong loop closure measurements. We extensively demonstrate on several applications (binocular SLAM, monocular SLAM and video mosaicking) that our approach not only exhibits a low computational time and detects wrong loop closures but also significantly outperforms the state of the art algorithm in terms of RMSE.

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 Linear Depth Estimation from an Uncalibrated, Monocular Polarisation Image
Nächstes Kapitel Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
Inference in the case of a single loop using the absolute parametrization is detailed in the supplementary material.
 
2
Equation (12) has the same form (up to a sign) as the cost function (4). Consequently, the highly efficient GN described in Sect. 4 can be applied to maximize (12).
 
Literatur
1.
Agarwal, P., Tipaldi, G.D., Spinello, L., Stachniss, C., Burgard, W.: Robust map optimization using dynamic covariance scaling. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 62–69. IEEE (2013)
2.
3.
Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATH
4.
Boumal, N., Singer, A., Absil, P.A.: Robust estimation of rotations from relative measurements by maximum likelihood. In: Proceedings of the 52nd Conference on Decision and Control, CDC (2013)
5.
Bourmaud, G., Mégret, R., Giremus, A., Berthoumieu, Y.: Global motion estimation from relative measurements in the presence of outliers. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9007, pp. 366–381. Springer, Heidelberg (2015)
6.
Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Homography based Kalman filter for mosaic building. applications to UAV position estimation. In: 2007 IEEE International Conference on Robotics and Automation, pp. 2004–2009. IEEE (2007)
7.
Carlone, L., Aragues, R., Castellanos, J.A., Bona, B.: A fast and accurate approximation for planar pose graph optimization. Int. J. Robot. Res. (2014)
8.
Chatterjee, A., Govindu, V.: Efficient and robust large-scale rotation averaging. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 521–528 (2013)
9.
Chirikjian, G.S.: Stochastic Models, Information Theory, and Lie Groups, vol. 2. Springer, Boston (2012)CrossRefMATH
10.
Choi, S., Zhou, Q.Y., Koltun, V.: Robust reconstruction of indoor scenes. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5556–5565. IEEE (2015)
11.
12.
Dubbelman, G., Browning, B.: COP-SLAM: closed-form online pose-chain optimization for visual SLAM. IEEE Trans. Robot. 31(5), 1194–1213 (2015)CrossRef
13.
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 834–849. Springer, Heidelberg (2014)
14.
Estrada, C., Neira, J., Tardós, J.D.: Hierarchical SLAM: real-time accurate mapping of large environments. IEEE Trans. Robot. 21(4), 588–596 (2005)CrossRef
15.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. (IJRR) (2013)
16.
Govindu, V.M.: Combining two-view constraints for motion estimation. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 2, pp. 218–225. IEEE (2001)
17.
Govindu, V.M.: Lie-algebraic averaging for globally consistent motion estimation. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1, pp. 684–691. IEEE (2004)
18.
Govindu, V.M.: Robustness in motion averaging. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 457–466. Springer, Heidelberg (2006)CrossRef
19.
Grisetti, G., Stachniss, C., Burgard, W.: Nonlinear constraint network optimization for efficient map learning. IEEE Trans. Intell. Transp. Syst. 10(3), 428–439 (2009)CrossRef
20.
21.
Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: a general framework for graph optimization. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3607–3613. IEEE (2011)
22.
Latif, Y., Cadena, C., Neira, J.: Robust loop closing over time for pose graph SLAM. Int. J. Robot. Res. (2013)
23.
Latif, Y., Cadena, C., Neira, J.: Robust graph slam back-ends: a comparative analysis. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 2683–2690. IEEE (2014)
24.
Meidow, J.: Efficient video mosaicking by multiple loop closing. In: Stilla, U., Rottensteiner, F., Mayer, H., Jutzi, B., Butenuth, M. (eds.) PIA 2011. LNCS, vol. 6952, pp. 1–12. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-24393-6_​1 CrossRef
25.
Meidow, J.: Efficient multiple loop adjustment for computer vision tasks. Photogrammetrie-Fernerkundung-Geoinformation 2012(5), 501–510 (2012)CrossRef
26.
Ramachandra, K.: Kalman Filtering Techniques for Radar Tracking. CRC Press, Boca Raton (2000)
27.
Roberts, R., Sinha, S.N., Szeliski, R., Steedly, D.: Structure from motion for scenes with large duplicate structures. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3137–3144. IEEE (2011)
28.
Sünderhauf, N., Protzel, P.: Switchable constraints for robust pose graph SLAM. In:2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1879–1884. IEEE (2012)
29.
Tron, R., Daniilidis, K.: Statistical pose averaging with non-isotropic and incomplete relative measurements. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 804–819. Springer, Heidelberg (2014)
30.
Zach, C.: Robust bundle adjustment revisited. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 772–787. Springer, Heidelberg (2014)
31.
Zach, C., Irschara, A., Bischof, H.: What can missing correspondences tell us about 3D structure and motion? In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
32.
Zach, C., Klopschitz, M., Pollefeys, M.: Disambiguating visual relations using loop constraints. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1426–1433. IEEE (2010)
33.
Zhang, J., Singh, S.: LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems Conference (RSS), Berkeley, CA, July 2014
Metadaten
Titel
Online Variational Bayesian Motion Averaging
verfasst von
Guillaume Bourmaud
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46483-1

Electronic ISBN: 978-3-319-46484-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46484-8_8