BIM-based Hybrid Inertial Positioning Approach · Saurabh Taneja and Asli Akcamete Problem...
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BIM-based Hybrid Inertial Positioning Approach Burcu Akinci, (CEE); James H. Garrett, Jr., (CEE); Lucio Soibelman (CEE) Saurabh Taneja and Asli Akcamete Problem Statement US Army Corps of Engineers BUILDING STRONG® Inertial data correction using weighted topology map-matching algorithm Inertial data correction using topologic curve- to-curve map-matching algorithm Map-matching data correction results Project Objectives Developing a proof-of concept prototype indoor navigation system -Leverage inertial sensing data -Recalibrate with geometric building information and WLAN -Extracting geometry and generating navigation network -Acquiring sensor data and correcting it using geometrical and topological information -Location information is the most important part of field context (Schilit, Adams and Roy 1994) - Accuracy of positioning systems affected in indoor environments due to multipath propagation and drift errors -Need to correct positioning data using spatial data Publications Taneja, S., B. Akinci, J.H. Garrett, Jr., E.W. East and L. Soibelman, (2011a). “Transforming an IFC-based Building Layout Information into a Geometric Network Model for Indoor Navigation Assistance,” ASCE Workshop on Computing in Civil Engineering, Miami, 19-22 June, 2011. Taneja, S., A. Akcamete, B. Akinci, J.H. Garrett, Jr., E.W. East and L. Soibelman, (2011b). “COBIE-based Lightweight Representation of a Building Navigation Network,” The 28th International Symposium of Automation and Robotics in Construction, Seoul, South Korea, June 29th-July 2nd, 2011. Taneja, S., B. Akinci, J.H. Garrett, Jr., E.W. East and L. Soibelman, (2011c). “BIM-based Hybrid Inertial Positioning for Facility Operations Support ,” CIB W-78 2011, 28th International Conference- Applications of IT in AEC industry, Sophia-Antipolis, France, 2011. Results and conclusions Two interesting observations- • High sensor drift should be frequently calibrated using building data • Geometry of the generated navigation network affects the accuracy of the corrected position data UML Component diagram Critical components of the prototype Research approach Map-matching algorithm
Transcript of BIM-based Hybrid Inertial Positioning Approach · Saurabh Taneja and Asli Akcamete Problem...
BIM-based Hybrid Inertial Positioning Approach Burcu Akinci, (CEE); James H. Garrett, Jr., (CEE); Lucio Soibelman (CEE)
Saurabh Taneja and Asli Akcamete
Problem Statement
US Army Corps of Engineers BUILDING STRONG®
Inertial data correction using weighted topology map-matching algorithm
Inertial data correction using topologic curve-to-curve map-matching algorithm
Map-matching data correction results
Project Objectives Developing a proof-of concept prototype indoor navigation system
- Leverage inertial sensing data - Recalibrate with geometric building information and WLAN - Extracting geometry and generating navigation network - Acquiring sensor data and correcting it using geometrical and topological information
- Location information is the most important part of field context (Schilit, Adams and Roy 1994)
- Accuracy of positioning systems affected in indoor environments due to multipath propagation and drift errors - Need to correct positioning data using spatial data
Publications Taneja, S., B. Akinci, J.H. Garrett, Jr., E.W. East and L. Soibelman, (2011a). “Transforming an IFC-based Building Layout Information into a Geometric Network Model for Indoor Navigation Assistance,” ASCE Workshop on Computing in Civil Engineering, Miami, 19-22
June, 2011. Taneja, S., A. Akcamete, B. Akinci, J.H. Garrett, Jr., E.W. East and L. Soibelman, (2011b). “COBIE-based Lightweight Representation of a Building Navigation Network,” The 28th International Symposium of Automation and Robotics in Construction, Seoul, South Korea,
June 29th-July 2nd, 2011. Taneja, S., B. Akinci, J.H. Garrett, Jr., E.W. East and L. Soibelman, (2011c). “BIM-based Hybrid Inertial Positioning for Facility Operations Support ,” CIB W-78 2011, 28th International Conference- Applications of IT in AEC industry, Sophia-Antipolis, France, 2011.
Results and conclusions
Two interesting observations- • High sensor drift should be frequently calibrated using building
data • Geometry of the generated navigation network affects the
accuracy of the corrected position data
UML Component diagram
Critical components of the prototype
Research approach
Map-matching algorithm
