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Leveraging Object Recognition in Reliable Vehicle Localization from Monocular Images

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1140)

Abstract

We present the processing pipeline of a monocular vision system that successfully performs the task of detecting, identifying and localizing a city bus electric charger station. This task is essential to the operation of an advanced driver assistance system that helps the driver to dock the long vehicle at the charging station. The focus is on the role of machine learning techniques in developing a robust detection and classification procedure that allows our system to localize the camera with respect to the charger even from long distances. We demonstrate that the learned detection procedure improves robustness of the vision techniques for monocular localization, while the geometric relations estimated by our system can be used to improve the learning results.

Notes

Acknowledgement

This work was funded by the National Centre for Research and Development grant POIR.04.01.02-00-0081/17.

References

  1. 1.
    Ćwian, K.: Feature-Based Laser Simultaneous Localization and Mapping for Automotive Applications. Agencja Wydawnicza Impuls, Kraków (2019)Google Scholar
  2. 2.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Rhode Island, pp. 3354–3361 (2012)Google Scholar
  3. 3.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, pp. 580–587 (2014)Google Scholar
  4. 4.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  5. 5.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2980–2988 (2017)Google Scholar
  6. 6.
    Kim, J., Cho, H., Hwangbo, M., Choi, J., Canny, J., Kwon, Y.P.: Deep traffic light detection for self-driving cars from a large-scale dataset. In: IEEE International Conference on Intelligent Transportation Systems, Maui, pp. 280–285 (2018)Google Scholar
  7. 7.
    Marchand, E., Spindler, F., Chaumette, F.: ViSP for visual servoing: a generic software platform with a wide class of robot control skills. IEEE Robot. Autom. Mag. 12(4), 40–52 (2005)CrossRefGoogle Scholar
  8. 8.
    Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)CrossRefGoogle Scholar
  9. 9.
    Nilwong, S., Hossain, D., Kaneko, S., Capi, G.: Deep learning-based landmark detection for mobile robot outdoor localization. Machines 7(2), 25 (2019)CrossRefGoogle Scholar
  10. 10.
    Nowak, T., Nowicki, M., Ćwian, K., Skrzypczyński, P.: How to improve object detection in a driver assistance system applying explainable deep learning. In: IEEE Intelligent Vehicles Symposium, Paris, pp. 226–231 (2019)Google Scholar
  11. 11.
    Nowicki, M., Nowak, T., Skrzypczyński, P.: Laser-based localization and terrain mapping for driver assistance in a city bus. In: Szewczyk, R., et al. (eds.) Automation 2019 Progress in Automation, Robotics and Measurement Technique, AISC 920, pp. 502–512. Springer (2019)Google Scholar
  12. 12.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 91–99 (2015)Google Scholar
  13. 13.
    Royer, E., Lhuillier, M., Dhome, M., Chateau T.: Localization in urban environments: monocular vision compared to a differential GPS sensor. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR, San Diego (2005)Google Scholar
  14. 14.
    Zhang, Q., Zhu, S.: Visual interpretability for deep learning: a survey, frontiers of information technology & electronic. Engineering 19(1), 27–39 (2018)Google Scholar
  15. 15.
    Zhu, N., Marais, J., Betaille, D., Berbineau, M.: GNSS position integrity in urban environments: a review of literature. IEEE Trans. Intell. Transp. Syst. 19(9), 2762–2778 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Control, Robotics, and Information EngineeringPoznań University of TechnologyPoznańPoland

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