A fuzzy multi-sensor architecture for indoor navigation

This paper presents an indoor navigation system based on sensor data from first responder wearable modules. The proposed system integrates data from an inertial sensor, a digital camera and a radio frequency identification device using a sophisticated fuzzy algorithm. To improve the navigation accuracy, different types of first responder activities and operational conditions were examined and classified according to extracted qualitative attributes. The vertical acceleration data, which indicates the periodic vibration during gait cycle, is used to evaluate the accuracy of the inertial based navigation subsystem. The amount of strong feature correspondences assess the quality of the three-dimensional scene knowledge from digital camera feedback. Finally, the qualitative attribute, in order to evaluate the efficiency of the radio frequency identification subsystem, is the degree of probability of each location estimate. Fuzzy if-then rules are then applied to these three attributes in order to carry out the fusion task. Simulation results based on the proposed architecture have shown better navigation effectiveness and lower positioning error compared with the used stand alone navigation systems.

[1]  Takeo Kanade,et al.  A Paraperspective Factorization Method for Shape and Motion Recovery , 1994, ECCV.

[2]  Steve Sawyer,et al.  Mobility and the first responder , 2004, CACM.

[3]  Henry Tirri,et al.  A Probabilistic Approach to WLAN User Location Estimation , 2002, Int. J. Wirel. Inf. Networks.

[4]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[5]  Stéphane Beauregard,et al.  Omnidirectional Pedestrian Navigation for First Responders , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[6]  S. Valiviita,et al.  Angular acceleration measurement: a review , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[7]  Reinhard Koch,et al.  Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[8]  Henrik Aanæs,et al.  Robust Factorization , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Antonios Gasteratos,et al.  Development of a stereo vision system for remotely operated robots: A control and video streaming architecture , 2008, 2008 IEEE Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems.

[10]  R. Jirawimut,et al.  Visual odometer for pedestrian navigation , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[11]  Cesare Alippi,et al.  A RSSI-based and calibrated centralized localization technique for wireless sensor networks , 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW'06).

[12]  Takeshi Kurata,et al.  Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[13]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[14]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[15]  Khaled Shuaib,et al.  Transmission of MPEG-2 Video Streams over ATM , 1998, IEEE Multim..

[16]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[17]  Phillip Tomé,et al.  Indoor Navigation of Emergency Agents , 2007 .

[18]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[19]  M. Kourogi,et al.  A method of personal positioning based on sensor data fusion of wearable camera and self-contained sensors , 2003, Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI2003..

[20]  Magdalena Balazinska,et al.  Challenges for Pervasive RFID-Based Infrastructures , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).

[21]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[22]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.