Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-07-03T09:18:56.208Z Has data issue: false hasContentIssue false
  • English
  • Français

A Novel Three Dimensional Movement Model for Pedestrian Navigation

Published online by Cambridge University Press:  12 March 2012

Mohammed Khider ,
Susanna Kaiser  and
Patrick Robertson
Mohammed Khider*
Affiliation:
(German Aerospace Center (DLR), Institute of Communication and Navigation, Germany.)
Susanna Kaiser
Affiliation:
(German Aerospace Center (DLR), Institute of Communication and Navigation, Germany.)
Patrick Robertson
Affiliation:
(German Aerospace Center (DLR), Institute of Communication and Navigation, Germany.)
*
(E-mail: mohammed.khider@dlr.de)
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, a Three Dimensional Pedestrian Movement Model (3D-MM) capable of probabilistically representing pedestrian movement in challenging indoor and outdoor localization environments is developed, implemented and evaluated. In the scope of this paper, the model is used to generate a ‘movement’ or a transition for dynamic positioning systems that are based on sequential Bayesian filtering techniques, such as particle filtering. It can also be used to assign weights for particles' movements proposed by sensors in Likelihood Particle Filters implementations. Alternatively, the developed model can be applied to other applications domains such as infrastructure design, evacuation planning, robot-human interaction and pervasive computing. The novelty of the model is in its ability to characterize both random and goals-oriented pedestrian motions and additionally use the a priori knowledge of maps and floor plans. It will be shown that an appropriate pedestrian movement model not only improves the positioning accuracy, but is also essential for a robust positioning estimator. Additionally, this work shows that maps and floor plans can improve pedestrian movement models but do not replace them, as several authors suggest.

Keywords

Mobility ModelsMovement ModelsIndoor PositioningMultisensor Navigation
Type
Research Article
Information
The Journal of Navigation , Volume 65 , Issue 2 , April 2012 , pp. 245 - 264
Copyright
Copyright © The Royal Institute of Navigation 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adam, A. and Amershi, S. (2004). Identifying Humans by Their Walk and Generating New Motions Using Hidden Markov Models. The University of British Columbia, Topics in AI: Graphical Models and Computer Animation, Technical Report.Google Scholar
Arulampalam, S., Maskell, S., Gordon, N. and Clapp, T. (2002). A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50, No. 2.CrossRefGoogle Scholar
Banos, A. and Charpentier, A. (2007). Simulating Pedestrian Behaviour in Subway Stations with Agents. Proceedings of the 4th European Social Simulation Association, Toulouse, France.Google Scholar
Bar-Shalom, Y., Rong Li, X. and Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation. John Wiley & Sons, Inc.Google Scholar
Beauregard, S., Widyawan, and Klepal, M. (2008). Indoor PDR Performance Enhancement Using Minimal Map Information and Particle Filters. Proceedings of the IEEE/ION Plans. Monterey, USA.CrossRefGoogle Scholar
Bekey, G. A. (1995). Walking, The Handbook of Brain Theory and Neural Networks. MIT Press.Google Scholar
Brakatsoulas, S., Pfoser, D., Wenk, C. and Salas, R. (2005). On Map-Matching Vehicle Tracking Data. VLDB.Google Scholar
Dijkstra, J., Jessurun, A. J. and Timmermans, H. J. P. (2001). A Multi-Agent Cellular, Automata Model of Pedestrian Movement, Pedestrian and Evacuation Dynamics. Springer-Verlag 2001, 173-181.Google Scholar
Green, R. D. and Guan, L. (2004). Quantifying and Recognizing Human Movement Patterns from Monocular Video Images - Part I: A New Framework for Modelling Human Motion. IEEE Transactions on Circuits and Systems for Video Technology, 2004. 179–190.CrossRefGoogle Scholar
Helbing, D. and Molnar, P. (1995). Social Force Model for Pedestrian Dynamics. Physical Review, E 51, 4282-4286.Google ScholarPubMed
Helbing, D. (1992). Models for Pedestrian Behaviour, Natural Structures. Principles, Strategies, and Models in Architecture and Nature, Part II. Sonderforschungsbereich 230, Stuttgart.Google Scholar
Hyytiä, E., Lassila, P. and Virtamo, J. (2006). Spatial Node Distribution of the Random Waypoint Mobility Model with Applications. IEEE Trans. Mobile Computing.CrossRefGoogle Scholar
Kammann, J., Angermann, M. and Lami, B. (2003). A New Mobility Model Based on Maps. VTC.Google Scholar
Khider, M., Kaiser, S., Robertson, P. and Angermann, M. (2008). A Novel Movement Model for Pedestrians Suitable for Personal Navigation. ION NTM, San Diego, California.Google Scholar
Khider, M., Kaiser, S., Robertson, P. and Angermann, M. (2009). A Three Dimensional Movement Model For Pedestrian Navigation. Proceedings of European Navigation Conference, Global Navigation Satellite Systems (ENC-GNSS), Napoli, Italy.Google Scholar
Krach, B. and Robertson, P. (2008). Integration of Foot-Mounted Inertial Sensors into a Bayesian Location Estimation Framework. Proceedings of 5th Workshop on Positioning, Navigation and Communication (WPNC 2008), Hannover, Germany.CrossRefGoogle Scholar
Lakoba, T. I., Kaup, D. J. and Finkelstein, N. M. (2005). Modifications of the Helbing-Molnár-Farkas-Vicsek Social Force Model for Pedestrian Evolution. Simulation, 81, 339-352.CrossRefGoogle Scholar
Lee, C. (1961). An Algorithm For Path Connections and its Applications. IRE Transactions on Electronic Computing, EC-10, 346–365.CrossRefGoogle Scholar
MacGregor-Smith, J. (1998). Evacuation Networks. Encyclopedia of Optimization.Google Scholar
Okazakia, S. and Matsushitaa, S. (1993). A Study of Simulation Model for Pedestrian Movement with Evacuation and Queuing. Proceedings of the International Conference on Engineering for Crowd Safety.Google Scholar
Osorio, C. and Bierlaire, M. (1998). An Analytic Finite Capacity Queuing Network Capturing Congestion and Spillbacks. Tristan VI, EPFL.Google Scholar
Pentland, and Liu, A. (1999). Modelling and Prediction of Human Behaviour. Neural Computation, 11, 229–242.CrossRefGoogle Scholar
Ressel, W. (2004). Modeling and Simulation of Mobility. Proceedings of 1st International Workshop on Intelligent Transportation (WIT 2004), Hamburg, Germany.Google Scholar
Rhee, I. (2008). On the Levy-Walk Nature of Human Mobility: Do Humans Walk like Monkey? Proceedings of the 27th Conference on Computer Communications, IEEE, 924-932, Phoenix, Arizona.CrossRefGoogle Scholar
Schmidt, G. K. and Azam, K. (1993). Mobile Robot Path Planning and Execution Based on a Diffusion Equation Strategy. Advanced Robotics, 7, 479–490.CrossRefGoogle Scholar
Scott, C. (1994). Improved GPS Positioning for Motor Vehicles Through Map Matching. Proceeding of ION GPS-94, Salt Lake City, USA.Google Scholar
Sharma, S. and Vishwamittar, (2005). Brownian Motion Problem: Random Walk and Beyond. The Journal of Science Education – Resonance, 49–66.Google Scholar
Teknomo, K. (2002). Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Data Collection and Simulation Model. PhD Dissertation.Google Scholar
Tradišauskas, N., Tiešytėdalia, D. and Jensen, C. S. (2004). A Study of Map Matching for GPS Positioned Mobile Objects. Proceeding of 7thWIM Meeting, Uppsala, Sweden.Google Scholar
Weifang, F., Lizhong, Y. and Fan, W. (2003). Simulation of Bi-Direction Pedestrian Movement Using a Cellular Automata Model. Science Direct, Physica A 321, 633–640.Google Scholar
Wendlandt, K., Khider, M., Angermann, M. and Robertson, P. (2006). Continuous Location and Direction Estimation with Multiple Sensors Using Particle Filtering. Proceeding of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.CrossRefGoogle Scholar
Woodman, O. and Harle, R. (2008). Pedestrian Localisation for Indoor Environments, Proceeding of UbiComp, ACM.CrossRefGoogle Scholar
Yang, L., Fang, W., Li, L., Huang, R. and Fan, W. (2003). Cellular Automata Pedestrian Movement Model Considering Human Behaviour. Chinese Science Bulletin – English Edition, 48; 1695-1699.CrossRefGoogle Scholar
Zhao, T. and Nevatia, R. (2002). 3D Tracking of Human Locomotion: A Tracking as Recognition Approach. Proceedings of 16th International Conference on Pattern Recognition.CrossRefGoogle Scholar