Fusion of data from inertial sensors, raster maps and GPS for estimation of pedestrian geographic location in urban terrain

• Autorzy: Barański P. , Polańczyk M. , Strumiłło P.
• Języki publikacji: EN

Abstrakty

EN

An electronic system and an algorithm for estimating pedestrian geographic location in urban terrain is reported in the paper. Different sources of kinematic and positioning data are acquired (i.e.: accelerometer, gyroscope, GPS receiver, raster maps of terrain) and jointly processed by a Monte-Carlo simulation algorithm based on the particle filtering scheme. These data are processed and fused to estimate the most probable geographical location of the user. A prototype system was designed, built and tested with a view to aiding blind pedestrians. It was shown in the conducted field trials that the method yields superior results to sole GPS readouts. Moreover, the estimated location of the user can be effectively sustained when GPS fixes are not available (e.g. tunnels).

Słowa kluczowe

EN

pedestrian navigation; data fusion; GPS; digital maps; particle filtering; electronic travel aids

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2011
• Tom: Vol. 18, nr 1
• Strony: 145--157
• Opis fizyczny: Bibliogr. 18 poz., rys., wykr.

Twórcy

autor

Barański P.

autor

Polańczyk M.

autor

Strumiłło P.

• Technical University of Lodz, Institute of Electronics, 211/215 Wolczanska, 90-924 Lodz, Poland,

Bibliografia

• [1] Fischer, C., Gellersen, H. (Jan.-Mar. 2010). Location and Navigation Support for Emergency Responders: A Survey. Pervasive Computing, 38-47.
• [2] Hersh, M.A., Johnson, M.A. (2008). Assistive Technology for Visually Impaired and Blind People. Springer-Verlag. London Limited
• [3] Helal, S., Mokhtari, M., Abdulrazak, B. (Eds). (2008). The Engineering Handbook of Smart Technology for Aging. Disability and Independence. John Wiley & Sons.
• [4] Arikawa, M., Konomi, S. (Jul.-Sept. 2007). Navitime: supporting pedestrian navigation in the real world. Pervasive Computing, 21-29.
• [5] Retscher, G., Thienelt, M. (2004). NAVIO - a navigation and guidance service for pedestrians. Journal of Global Positioning Systems, 3(1-2), 208-217.
• [6] Mezentsev, O., Lachapelle, G., Collin J. (2005). Pedestrian dead reckoning - a solution to navigation in GPS signal degraded areas. Geomatica, 59(2), 175-182.
• [7] Fang, L., Antsaklis, P.J., Montestruque, L., McMickell, M.B., Lemmon, M., Sun, Y., Fang, H., Koutroulis, I., Haenggi, M., Xie, M., Xie, X. (2005). Design of a Wireless Assisted Pedestrian Dead Reckoning System - The NavMote Experience. IEEE Trans. on Instrumentation and Measurement, 54(6), 2342-2358.
• [8] Kourogi, M., Sakata, N., Okuma, T., Kurata, T. (2006). Indoor/Outdoor Pedestrian Navigation with an Embedded GPS/RFID/Self-contained Sensor System. Proc. Int. Conf. Artificial Reality and Telexistence CAT2006,1310-1321.
• [9] The Smith-Kettlewell Eye Research Institute. (2010 November) www.ski.org.
• [10] Farell, J. A. (2008). Aided Navigation. McGraw Hill. New York.
• [11] Baranski, P., Strumillo, P. (2010). An electronic module implementing dead-reckoning algorithm for improving geographic location accuracy from GPS readouts in urban terrain. VIII-th National Electronics Conference. 07-10 June 2010. (in Polish).
• [12] The website of Analog Devices. www.analog.com/static/imported-files/data_sheets/ADIS16355.pdf. (2011 February).
• [13] Arulampalam, M.S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processing, 50, 174-188.
• [14] Ceranka, S., Niedźwiecki, M. (2003). Application of Particle Filtering in Navigation System for the Blind. Proceedings of the IEEE Seventh International Symposium on Signal Processing and its Applications.
• [15] Brown R. (1983). Introduction to Random Signal Analysis And Kalman Filtering. John Wiley & Sohns.
• [16] Cappe O., Godsill S., and Moulines E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceeding of the IEEE, 95, 899-924.
• [17] Mandel J. (2007). A Brief Tutorial on the Ensemble Kalman Filter. University of Colorado at Denver and Health Sciences Center.
• [18] Mackenzie D. (October 2003). Ensemble Kalman Filters Bring Weather Models Up to Date. SIAM News, 36(8), 1-4.