German Aerospace Center (DLR)
Institute of Communications and Navigation
Department Communications Systems
Broadband Systems Group

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Sensor Fusion for Indoor Navigation

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Latest updates September 2009 The movies and papers for FootSLAM are now available here! May 2009 The reference data sets for multisensor pedestrian navigation that are introduced in our paper M. Angermann, A. Friese, M. Khider, B. Krach, K. Krack, P. Robertson A Reference Measurement Data Set for Multisensor Pedestrian Navigation with Accurate Ground Truth [PDF] European Navigation Conference (ENC-GNSS 2009), Naples, Italy, May 2009 are now available at our download page.

General information

Positioning in buildings and other environments where GNSS reception is difficult will require a combination of sensors and other information such as building plans in order to function accurately. We are pursuing sensor fusion approaches that combine GNSS, foot mounted inertial sensors electronic compasses, baro-altimeters, maps and active RFID tags. A particularly powerful combination is INS step measurement in conjunction with maps which can converge to the correct position after less than a minute of motion. We have developed a two-layer sensor fusion architecture that operates with a Kalman filter where possible, and fuses other sensors and maps at a higher-level, lower rate, particle filter. In buildings, a few dispersed RFID tags or even moderately GNSS reception can significantly aid the overall positioning.

Combining complementary sensors through an optimal sensor fusion algorithm leads to synergetic effects and thus improves positioning.	Two-layer sensor fusion architecture that operates with a Kalman filter for the inertial stride estimation. Other sensors sensors and maps are fused at a higher-level, lower rate, particle filter.	Integration with map-matching in the particle filter: A pedestrian wearing the foot-mounted sensor walked the indicated track (black line). At each figure the posterior position estimate (green) becomes increasingly accurate, after 80s it is unimodal. Watch video [MPG].	Illustration of the pedestrian track that was estimated by a particle filter based upon our reference data set "March09Measurement09".

Videos and other media

• Youtube:
  
  
  
  
• Video: Fusion of GPS, foot-mounted INS, compass, baro-altimeter and building maps via a particle filter and a Kalman filter [AVI]
• Video: Fusion of WLAN fingerprinting and a foot-mounted INS using an extended Kalman filter (work done in the EU-Project Daidalos): [AVI]
• Video: Particles converging to the true position using a foot-mounted INS and map-aiding only [MPG]
• Presentation on the integration of foot-mounted inertial sensors at PLANS 2008 [PDF]
• Indoor positioning overview poster [PDF]

Papers

You can follow the ELIB link to our internal electronic library where you can download the paper as PDF and retrieve citation information, e.g. for BibTeX. Additional papers are available on our pages on related topics (links at the top left of this page).

• B. Krach, P. Robertson
  Cascaded Estimation Architecture for Integration of Foot-Mounted Inertial Sensors [ELIB]
  Proc. 2008 IEEE/ION Position Location and Navigation Symposium (IEEE/ION PLANS 2008),
  Monterey, California, USA, Mai 2008


• M. Khider, S. Kaiser, P. Robertson, M. Angermann
  The Effect of Maps-Enhanced Novel Movement Models on Pedestrian Navigation Performance [ELIB]
  Proc. 12th European Navigation Conference (ENC GNSS 2008),
  Toulouse, Frankreich, Apr. 2008


• B. Krach, P. Robertson
  Integration of Foot-Mounted Inertial Sensors into a Bayesian Location Estimation Framework [ELIB]
  Proc. 5th Workshop on Positioning, Navigation and Communication 2008 (WPNC 2008),
  Hannover, Germany, Mar. 2008


• M. Khider, S. Kaiser, P. Robertson, M. Angermann
  A Novel Movement Model for Pedestrians Suitable for Personal Navigation [ELIB]
  Proc. 2008 International Technical Meeting of the Institute of Navigation Satellite Division (ION ITM 2008),
  San Diego, California, USA, Jan. 2008


• K. Wendlandt, P. Robertson, M. Khider, M. Angermann, K. Sukchaya
  Demonstration of a Realtime Active-Tag RFID, Java Based Indoor Localization System using Particle Filtering [ELIB]
  Adjunct Proc. 9th International Conference on Ubiquitous Computing (UBICOMP 2007),
  Innsbruck, Austria, Sep. 2007


• K. Wendlandt, M. Khider, M. Angermann, P. Robertson
  Continuous location and direction estimation with multiple sensors using particle filtering [ELIB]
  Proc. 2006 International Conference in Multisensor Fusion and Integration for Intelligent Systems (MFI 2006),
  Heidelberg, Germany, Sep. 2006


• K. Wendlandt, P. Robertson, M. Berbig
  Indoor Localization with Probability Density Functionsd based on Bluetooth [ELIB]
  Proc. 2005 Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2005),
  Berlin, Germany, Sep. 2005


• M. Angermann, J. Kammann, P. Robertson, A. Steingass, T. Strang
  Software Representation for Heterogeneous Data Sources Within A Probabilistic Framework [ELIB]
  Proc. 2001 International Symposium on Location Based Services for Cellular Users (LOCELLUS 2001),
  Munich, Germany, Feb. 2001


• Mohammed Khider
  Implementation of a Simulator/Demonstrator for the SoftLocation Concept using Bayesian Filters, [ELIB][]
  Masterarbeit, S. 166, Universität Ulm (Ingenieurwissenschaften und Informatik) ,
  Ulm, Germany, 2005

Links

• Homepage of the PLAN Group at the University of Calgary: http://plan.geomatics.ucalgary.ca/
• Homepage of Oliver Woodman at the University of Cambridge: http://www.cl.cam.ac.uk/~ojw28/
• Sequential Monte Carlo Methods Homepage: http://www-sigproc.eng.cam.ac.uk/smc/