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MNISIKLIS: Indoor LBS for All. Vassilis Papataxiarhis , V.Riga , V. Nomikos , O.Sekkas , K.Kolomvatsos , V.Tsetsos , P. Papageorgas , S.Vourakis , S.Hadjiefthymiades , and G. Kouroupetroglou vpap@di.uoa.gr Department of Informatics and Telecommunications University of Athens – Greece
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MNISIKLIS: Indoor LBS for All VassilisPapataxiarhis, V.Riga, V. Nomikos, O.Sekkas, K.Kolomvatsos, V.Tsetsos, P. Papageorgas, S.Vourakis, S.Hadjiefthymiades, and G. Kouroupetroglou vpap@di.uoa.gr Department of Informatics and Telecommunications University of Athens – Greece LBS-2008, 26-28 Nov. 2008, Salzburg
Introduction MNISIKLIS - Project details National Project (July ’06 – December ‘07) Consortium Main Goal Provide universal indoor LBSs focusing on navigation Unisystems S.A. National and Kapodistrian University of Athens (NKUA) Technological Educational Institute of Piraeus
Services Static Navigation Dynamic Navigation Where-Am-I Service Exploration Service Nearest Points-Of-Interest (POI)
Positioning Subsystem Sensing technologies UHF RFIDs (i.e tags and reader) WiFi access points (RSSI) dead reckoning for pedestrian users 3-axis electronic compass 3-axis accelerometer Fusion techniques two levels of data fusion in the Location Server 1rst level: RFID tags and WLAN measurements 2nd level: 1rst level output + DR estimation
Sensing Components Sensor unit attached to the user’s belt data transfer through Bluetooth to the PDA Dead reckoning filter raw accelerometer data step detection step length estimation predicted walking distance of m steps Data collector (PDA) executes the DR algorithm collects RFID and RSSI measurements data transfer through WLAN to the Location Server
Location Server (1/3) • N symbolic locations Li • bidirectional communication with the data collector • two levels of fusion • final estimation of user’s position
Location Server (2/3) Communication component: communication with the data collector validation of received data quantization of the WLAN RSSI values received vector’s format: <userId,IE_Idi=valuei,X=x1,Y=y1,Z=z1,Orientation> 1rst level fusion engine: Dynamic Bayesian Network (DBN) based on previous estimation of user's position probability distributions - XMLBIF file Output: <Pb1,Pb2,…,PbN>
Location Server (3/3) DR data converter: 2D Euclidean distance (di) definition of threshold dt probability reversely proportional to di2 Output: <Pc1,Pc2,…,PcN > 2nd level fusion engine: weights wb+wc=1 combination formula: Pi=wb*Pbi+wc*Pci Output: location with the highest probability feedback for the DR and DBN update database
Metadata Expressed in terms of OWL ontologies Spatial model instantiation through GIS metadata Spatial Database Instances Creation Algorithm Ontology Instances
Core Navigation Algorithm Hybrid rule-based algorithm. Takes into account : Route complexity Euclidean route distance User profile (capabilities and preferences) Steps: Create “user compatible” building graph based on user profile and application of access rules (in terms of SWRL) E.g. WheelChaired_User(?x) ^ Stairway(?y) isObstacleFor(?y,?x) Find the k-simplest paths Assign the total cost of each path as a function of rewards and penalties of the total path distance, preferences and perceptual rules
User Interaction Subsystem Main user groups Non-disabled Elderly With vision loss Locomotive disabled Multimodal Interfaces Visual, Audio and Haptic Modality Devices PDA, Tablet PC, Smart Phone, Mobile Phone Head-mounted screen, braille display, earphones
Functionality of UI subsystem Turn-by-turn algorithm Left/right turns Distances Info about near doors SVG map capabilities E.g., zooming, turning, moving Related photo 3 levels of detail
User Evaluation 20 users (5 per user group) 3 predefined scenarios Evaluation through questionnaires Positive Comments Dynamic Navigation Menu Sufficient instructions Negative Comments Delay in the delivery of instructions
Implementation Details ESRI ArcGIS software PostGIS spatial DB Batik SVG Toolkit (Apache Foundation) Protégé Ontology Editor Knowledge Representation Languages Ontology models in OWL-DL SWRL rules Jess for reasoning over SWRL Mascopt Library for graph creation and path search
Contributions and Future Work Main Contributions UHF RFID for proximity sensing Multi-sensor fusion process Multimodal user interfaces Human centered service logic Future Work Landmark-based navigation Kalman filtering for DR Path prediction techniques
QUESTIONS? Thank you! http://speech.di.uoa.gr/mnisiklis