Skip to main content
SpringerLink
Log in
Book cover

International Conference on Modelling and Simulation for Autonomous Systems

MESAS 2018: Modelling and Simulation for Autonomous Systems pp 456–476Cite as

Adaptive Image Processing Methods for Outdoor Autonomous Vehicles

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11472))

Abstract

This paper concerns adaptive image processing for visual teach-and-repeat navigation systems of autonomous vehicles operating outdoors. The robustness and the accuracy of these systems rely on their ability to extract relevant information from the on-board camera images, which is then used for the autonomous navigation and the map building. In this paper, we present methods that allow an image-based navigation system to adapt to a varying appearance of outdoor environments caused by dynamic illumination conditions and naturally occurring environment changes. In the performed experiments, we demonstrate that the adaptive and the learning methods for camera parameter control, image feature extraction and environment map refinement allow autonomous vehicles to operate in real, changing world for extended periods of time.

The work has been supported by the project 17-27006Y.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  2. Biber, P., Duckett, T.: Dynamic maps for long-term operation of mobile service robots. In: RSS (2005)

    Google Scholar 

  3. Blanc, G., Mezouar, Y., Martinet, P.: Indoor navigation of a wheeled mobile robot along visual routes. In: International Conference on Robotics and Automation (ICRA) (2005)

    Google Scholar 

  4. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Proceedings of the ICCV (2010)

    Google Scholar 

  5. Carlevaris-Bianco, N., Eustice, R.M.: Learning visual feature descriptors for dynamic lighting conditions. In: IROS. IEEE (2014)

    Google Scholar 

  6. Chen, Z., Birchfield, S.T.: Qualitative vision-based path following. IEEE Trans. Rob. Autom. 25(3), 749–754 (2009)

    Article  Google Scholar 

  7. Churchill, W.S., Newman, P.: Experience-based navigation for long-term localisation. IJRR 32(14), 1645–1661 (2013). https://doi.org/10.1177/0278364913499193

    Article  Google Scholar 

  8. Dayoub, F., Duckett, T.: An adaptive appearance-based map for long-term topological localization of mobile robots. In: IROS (2008)

    Google Scholar 

  9. De Cristóforis, P., et al.: Real-time monocular image-based path detection. J. Real Time Image Process. 11, 335–348 (2013)

    Article  Google Scholar 

  10. Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: SIGGRAPH. ACM (2008)

    Google Scholar 

  11. DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 237–267 (2002). https://doi.org/10.1109/34.982903

    Article  Google Scholar 

  12. Dvořáková, E.: Temporal models for mobile robot visual navigation. B.S. thesis, Czech Technical Univerzity in Prague (2018)

    Google Scholar 

  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. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_54

    Chapter  Google Scholar 

  14. Gadd, M., Newman, P.: Checkout my map: Version control for fleetwide visual localisation. In: IROS. IEEE/RSJ (2016)

    Google Scholar 

  15. Halodová, L.: Map management for long-term navigation of mobile robots. Bachelor thesis, Czech Technical University, May 2018

    Google Scholar 

  16. Halodová, L., Krajník, T.: Exposure setting for visual navigation of mobile robots. In: Student Conference on Planning in AI and Robotics (PAIR) (2017)

    Google Scholar 

  17. Holmes, S., Klein, G., Murray, D.W.: A square root unscented Kalman filter for visual monoSLAM. In: International Conference on Robotics and Automation (ICRA) (2008)

    Google Scholar 

  18. Kosaka, A., Kak, A.C.: Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties. CVGIP: Image Underst. 56(3), 271–329 (1992)

    Article  Google Scholar 

  19. Krajník, T., Cristóforis, P., Nitsche, M., Kusumam, K., Duckett, T.: Image features and seasons revisited. In: European Conference on Mobile Robots (ECMR) (2015)

    Google Scholar 

  20. Krajník, T., Majer, F., Halodová, L., Vintr, T.: Navigation without localisation: reliable teach and repeat based on the convergence theorem. In: IROS (2018)

    Google Scholar 

  21. Krajník, T., et al.: Image features for visual teach-and-repeat navigation in changing environments. Rob. Auton. Syst. 88, 127–141 (2017)

    Article  Google Scholar 

  22. Krajník, T., et al.: Long-term topological localization for service robots in dynamic environments using spectral maps. In: IROS (2014)

    Google Scholar 

  23. Krajník, T., et al.: FreMEN: frequency map enhancement for long-term mobile robot autonomy in changing environments. IEEE Trans. Rob. 33(4), 964–977 (2017)

    Article  Google Scholar 

  24. Kunze, L., Hawes, N., Duckett, T., Hanheide, M., Krajnik, T.: Artificial intelligence for long-term robot autonomy: a survey. IEEE RAL 3(4), 4023–4030 (2018). https://doi.org/10.1109/LRA.2018.2860628

    Article  Google Scholar 

  25. Latif, Y., Garg, R., Milford, M., Reid, I.: Addressing challenging place recognition tasks using generative adversarial networks. In: ICRA (2018)

    Google Scholar 

  26. Linegar, C., Churchill, W., Newman, P.: Work smart, not hard: recalling relevant experiences for vast-scale but time-constrained localisation. In: ICRA (2015)

    Google Scholar 

  27. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  28. Lowry, S., Milford, M.J.: Supervised and unsupervised linear learning techniques for visual place recognition in changing environments. IEEE T-RO 32(3), 600–613 (2016)

    Article  Google Scholar 

  29. Lowry, S., et al.: Visual place recognition: a survey. IEEE T-RO 32(1), 1–19 (2016)

    Article  Google Scholar 

  30. Lu, H., Zhang, H., Yang, S., Zheng, Z.: Camera parameters auto-adjusting technique for robust robot vision. In: ICRA. IEEE (2010)

    Google Scholar 

  31. Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15552-9_14

    Chapter  Google Scholar 

  32. Majer, F., Halodová, L., Krajník, T.: Source codes: bearing-only navigation. http://bearnav.eu

  33. Majer, F., et al.: A versatile visual navigation system for outdoor autonomous vehicles. In: Modeling and Simulation for Autonomous Systems (2018, in review)

    Google Scholar 

  34. Matsumoto, Y., Inaba, M., Inoue, H.: Visual navigation using view-sequenced route representation. In: International Conference on Robotics and Automation (ICRA) (1996)

    Google Scholar 

  35. Mühlfellner, P., Bürki, M., Bosse, M., Derendarz, W., Philippsen, R., Furgale, P.: Summary maps for lifelong visual localization. J. Field Rob. 33(5), 561–590 (2016)

    Article  Google Scholar 

  36. Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Rob. 31(5), 1147–1163 (2015). https://doi.org/10.1109/TRO.2015.2463671

    Article  Google Scholar 

  37. Neubert, P., Sunderhauf, N., Protzel, P.: Appearance change prediction for long-term navigation across seasons. In: European Conference on Mobile Robotics (2013)

    Google Scholar 

  38. Neves, A.J.R., Cunha, B., Pinho, A.J., Pinheiro, I.: Autonomous configuration of parameters in robotic digital cameras. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. LNCS, vol. 5524, pp. 80–87. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02172-5_12

    Chapter  Google Scholar 

  39. Paton, M., MacTavish, K., Berczi, L.-P., van Es, S.K., Barfoot, T.D.: I can see for miles and miles: an extended field test of visual teach and repeat 2.0. In: Hutter, M., Siegwart, R. (eds.) Field and Service Robotics. SPAR, vol. 5, pp. 415–431. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67361-5_27

    Chapter  Google Scholar 

  40. Porav, H., Maddern, W., Newman, P.: Adversarial training for adverse conditions: robust metric localisation using appearance transfer. In: ICRA (2018)

    Google Scholar 

  41. Rosen, D.M., Mason, J., Leonard, J.J.: Towards lifelong feature-based mapping in semi-static environments. In: ICRA. IEEE (2016)

    Google Scholar 

  42. Royer, E., Lhuillier, M., Dhome, M., Lavest, J.M.: Monocular vision for mobile robot localization and autonomous navigation. Int. J. Comput. Vis. 74(3), 237–260 (2007)

    Article  Google Scholar 

  43. Segvic, S., Remazeilles, A., Diosi, A., Chaumette, F.: Large scale vision based navigation without an accurate global reconstruction. In: CVPR (2007)

    Google Scholar 

  44. Shim, I., Lee, J.Y., Kweon, I.S.: Auto-adjusting camera exposure for outdoor robotics using gradient information. In: IROS. IEEE/RSJ (2014)

    Google Scholar 

  45. Krajník, T., Faigl, J., Vonásek, V., et al.: Simple, yet stable bearing-only navigation. J. Field Rob. 27(5), 511–533 (2010)

    Article  Google Scholar 

  46. Zhang, N., Warren, M., Barfoot, T.: Learning place-and-time-dependent binary descriptors for long-term visual localization. In: ICRA. IEEE (2016)

    Google Scholar 

  47. Zhang, Z., Forster, C., Scaramuzza, D.: Active exposure control for robust visual odometry in HDR environments. In: ICRA (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence Center, FEE, Czech Technical University, Prague, Czech Republic

    Lucie Halodová, Eliška Dvořáková, Filip Majer, Jiří Ulrich, Tomáš Vintr & Tomáš Krajník

  2. University of Nottingham, Nottingham, UK

    Keerthy Kusumam

Authors
  1. Lucie Halodová
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eliška Dvořáková
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Filip Majer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiří Ulrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tomáš Vintr
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Keerthy Kusumam
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tomáš Krajník
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomáš Krajník .

Editor information

Editors and Affiliations

  1. NATO Modelling and Simulation Centre of Excellence, Rome, Italy

    Jan Mazal

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Halodová, L. et al. (2019). Adaptive Image Processing Methods for Outdoor Autonomous Vehicles. In: Mazal, J. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2018. Lecture Notes in Computer Science(), vol 11472. Springer, Cham. https://doi.org/10.1007/978-3-030-14984-0_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14984-0_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14983-3

  • Online ISBN: 978-3-030-14984-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation