Autonomous Recognition of Emergency Site by Wearable Sensors

To support rescue activities of first responders is crucial at disaster sites. Especially, provisioning location and situation information is indispensable for those first responders to efficiently rescue injured people in unknown places with a lot of buildings such as private properties (like factories) and university campus. In such an extreme situation, seamless indoor/outdoor maps will be of substantial aid for the first responders. In this paper, we propose a method of creating an indoor/outdoor map by a first responder team. We assume some of them are equipped with range scanners and all the members have GPS and WiFi devices. Then the presence of obstacles and movable areas is estimated by combining information from different sources (like GPS, WiFi and range scanners) with different confident levels. Since these confident levels depend on scenarios and environments, we design an "adaptive information fusion" algorithm that automatically estimates the confident levels to optimize the precision of the generated map. We have demonstrated our method in several experiments with real sensor data.

[1]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[2]  Chris D. Nugent,et al.  Evidential fusion of sensor data for activity recognition in smart homes , 2009, Pervasive Mob. Comput..

[3]  Stephane Beauregard,et al.  A Helmet-Mounted Pedestrian Dead Reckoning System , 2006 .

[4]  Hui Fang,et al.  Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience , 2005, IEEE Transactions on Instrumentation and Measurement.

[5]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[6]  W. C. Jakes,et al.  Mobile Radio Propagation , 1974 .

[7]  Naser El-Sheimy,et al.  The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning Instruments , 2006, IEEE Transactions on Instrumentation and Measurement.

[8]  Andrew Howard,et al.  Multi-robot Simultaneous Localization and Mapping using Particle Filters , 2005, Int. J. Robotics Res..

[9]  Josef Kittler,et al.  Sum Versus Vote Fusion in Multiple Classifier Systems , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Hirozumi Yamaguchi,et al.  Map estimation using GPS-equipped mobile wireless nodes , 2010, Pervasive Mob. Comput..

[11]  Hirozumi Yamaguchi,et al.  Clearing a Crowd: Context-Supported Neighbor Positioning for People-Centric Navigation , 2012, Pervasive.

[12]  Sameer Singh,et al.  Approaches to Multisensor Data Fusion in Target Tracking: A Survey , 2006, IEEE Transactions on Knowledge and Data Engineering.

[13]  Yaser P. Fallah,et al.  Crowd Sourcing Indoor Maps with Mobile Sensors , 2010, MobiQuitous.

[14]  Jie Gao,et al.  Boundary recognition in sensor networks by topological methods , 2006, MobiCom '06.

[15]  Lawrence Wai-Choong Wong,et al.  A robust dead-reckoning pedestrian tracking system with low cost sensors , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[16]  Hirozumi Yamaguchi,et al.  Local map generation using position and communication history of mobile nodes , 2010, 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[17]  Alberto Broggi,et al.  Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion , 2007, IEEE Transactions on Intelligent Transportation Systems.

[18]  Vincent K. N. Lau,et al.  The Mobile Radio Propagation Channel , 2007 .

[19]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.