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Evaluation of Pose Hypotheses by Image Feature Extraction for Vehicle Localization

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Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

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Abstract

For the realization of driving assistance and safety systems vehicles are being increasingly equipped with sensors. As these sensors contribute a lot to the cost of the whole package and at the same time consume some space, car manufacturers try to integrate applications that make use of already integrated sensors. This leads to the fact that each sensor has to fulfil several functions at once and to deliver information to different applications. When estimating very precise positioning information of a vehicle existing sensors have to be combined in an appropriate way to avoid the integration of additional sensors into the vehicle.

The GPS receiver, which is coupled with the navigation assistant of the vehicle, delivers a rough positioning information, which has to be improved using already available information from other built in sensors. The approach discussed in this paper uses a model-based method to compare building models obtained from maps with video image information. We will examine, if the explorative coupling of sensors can deliver an appropriate evaluation criteria for positioning hypotheses.

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References

  1. Nischwitz, A., Fischer, M., Haberäcker, P.: Computer Graphics and Image Processing, pp. 67–68. Friedr. Vieweg u. Sohn Verlag, GWV Fachbuchverlag, Wiesbaden (2007) (in German)

    Google Scholar 

  2. Lowe, D.G.: Fitting Parameterized Three-Dimensional Models to Images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 441–450 (1991)

    Google Scholar 

  3. Harris, C., Stennet, C.: RAPID - A Video Rate Object Tracker. In: Proceedings of the British Machine Vision Conference, pp. 73–77 (1990)

    Google Scholar 

  4. Amstrong, M., Zissermann, A.: Robust object tracking. In: Proceedings of the Asian Conference on Computer Vision, pp. 58–62 (1995)

    Google Scholar 

  5. Lepetit, V., Fua, P.: Monocular Model-Based 3D Tracking of Rigid Objects: A Survey. In: Foundations and Trends in Computer Graphics and Vision (2005)

    Google Scholar 

  6. Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proceedings of the International Conference on Computer Vision (2003)

    Google Scholar 

  7. Davison, A.J., Murray, D.W.: Mobile Robot Localisation Using Active Vision. In: Proceedings of Fifth European Conference on Computer Vision, pp. 809–825 (1998)

    Google Scholar 

  8. Dellart, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo Localisation for Mobile Robots. In: IEEE International Conference on Robotics and Automation (1999)

    Google Scholar 

  9. Mathias, A., Kanther, U., Heidger, R.: Insideness and collision detection algorithm. In: Proc. Tyrrhenian International Workshop on Digital Communications - Enhanced Surveillance of Aircraft and Vehicles (2008)

    Google Scholar 

  10. Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. In: Readings in computer vision: issues, problems, principles and paradigms, pp. 726–740 (1987)

    Google Scholar 

  11. Schönherr, K., Giesler, B., Knoll, A.: Vehicle Localization by Utilization of Map-based Outline Information and Grayscale Image Extraction. In: Proceedings of the International Conference on Computer Graphics and Imaging (2010)

    Google Scholar 

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Schönherr, K., Giesler, B., Knoll, A. (2010). Evaluation of Pose Hypotheses by Image Feature Extraction for Vehicle Localization. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_68

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  • DOI: https://doi.org/10.1007/978-3-642-13208-7_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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