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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-334db791-3c1d-44d3-9035-fd438aab0668

Czasopismo

Archiwum Fotogrametrii, Kartografii i Teledetekcji

Tytuł artykułu

Motion estimation by integrated low cost system (vision and MEMS) for positioning of a scooter "Vespa"

Autorzy Guarnieri, A.  Milan, N.  Pirotti, F.  Vettore, A. 
Treść / Zawartość
Guarnieri.pdf
Warianty tytułu
Języki publikacji EN
Abstrakty
EN In the automotive sector, especially in these last decade, a growing number of investigations have taken into account electronic systems to check and correct the behaviour of drivers, increasing road safety. The possibility to identify with high accuracy the vehicle position in a mapping reference frame for driving directions and best-route analysis is also another topic which attracts lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate time by time the position, orientation and velocity of the system. To this aim low cost GPS and MEMS (sensors can be used. In comparison to a four wheel vehicle, the dynamics of a two wheel vehicle (e.g. a scooter) feature a higher level of complexity. Indeed more degrees of freedom must be taken into account to describe the motion of the latter. For example a scooter can twist sideways, thus generating a roll angle. A slight pitch angle has to be considered as well, since wheel suspensions have a higher degree of motion with respect to four wheel vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a motorcycle (“Vespa” scooter), which can be used as alternative to the “classical” approach based on the integration of GPS and INS sensors. Position and orientation of the scooter are derived from MEMS data and images acquired by on-board digital camera. A Bayesian filter provides the means for integrating the data from MEMS-based orientation sensor and the GPS receiver.
Słowa kluczowe
PL mobile mapping   czujnik orientacji   przetwarzanie obrazu   IMU   wizja komputerowa  
EN mobile mapping   orientation sensor   image transformation   IMU   computer vision  
Wydawca Zarząd Główny Stowarzyszenia Geodetów Polskich
Czasopismo Archiwum Fotogrametrii, Kartografii i Teledetekcji
Rocznik 2011
Tom Vol. 22
Strony 147--158
Opis fizyczny Bibliogr. 9 poz.
Twórcy
autor Guarnieri, A.
  • CIRGEO – Interdept. Research Center in Cartography, Photogrammetry, Remote Sensing and GIS, University of Padua, alberto.guarnieri@unipd.it
autor Milan, N.
  • CIRGEO – Interdept. Research Center in Cartography, Photogrammetry, Remote Sensing and GIS, University of Padua, nicola.milan@unipd.it
autor Pirotti, F.
  • CIRGEO – Interdept. Research Center in Cartography, Photogrammetry, Remote Sensing and GIS, University of Padua, francesco.pirotti@unipd.it
autor Vettore, A.
  • CIRGEO – Interdept. Research Center in Cartography, Photogrammetry, Remote Sensing and GIS, University of Padua, antonio.vettore@unipd.it
Bibliografia
1. Bevly, D.M. Cobb S. 2010. GNSS for Vehicle Control. Artech House, Boston USA.
2. Duda R.O., Hart P. E. 1972. Use of the Hough Transformation to Detect Lines and Curves in Pictures. Communications of the ACM, 15, pp. 11–15.
3. El-Sheimy N., Schwarz K.P. 1994. Integrating differential GPS receivers with an INS and CCD cameras for mobile GIS data collection. In: proceedings of Commission II Symposium, 6-10 June, Ottawa Canada, pp. 241-248.
4. Frezza R., Vettore A. 2001. Motion estimation by vision for mobile mapping with a motorcycle. In: 3rd International Symposium on Mobile Mapping Technology, Cairo, Egypt, 3-5 January.
5. Gustafsson F., Gunnarsson F., Bergman N., Forssell U., Jansson J., Karlsson R., Nordlund P. J. 2001. Particle Filters for Positioning, Navigation and Tracking. In: IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 425-437.
6. Nori F., Frezza R. 2003. Accurate reconstruction of the path followed by a motorcycle from on board camera images. In: IEEE Intelligent Vehicles Symposium, pp. 260-264.
7. Limebeer D.J.N., Sharp R.S. 2006. Bicycles, motorcycles and models. Control Systems Magazine IEEE 26(5), pp. 34-61.
8. Qi H., Moore J. B. 2002. Direct Kalman Filtering Approach for GPS/INS Integration. In : IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 2, pp. 687-693
9. Whipple, F.J.W. 1899. The stability of the motion of a bicycle. Quarterly Journal of Pure and Applied Mathematics, 30 pp. 312-348.
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