Interactive Virtual Indoor Navigation System using Visual Recognition and Pedestrian Dead Reckoning Techniques

Chin, Gee Low; Lee, Yunli

doi:10.14257/ijseia.2015.9.8.02

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Point clouds can also be utilized to detect potential obstacles (Vilariño et al, 2016). Such models could be developed as interactive and context-sensitive by utilizing the sensor on the mobile devices which include built-in accelerometer, compass and camera capabilities (Low et al, 2015).…”

Section: D Data Collection and Reconstructionmentioning

confidence: 99%

3d Geospatial Indoor Navigation for Disaster Risk Reduction and Response in Urban Environment

Ghawana¹,

Aleksandrov

Zlatanova

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Disaster management for urban environments with complex structures requires 3D extensions of indoor applications to support better risk reduction and response strategies. The paper highlights the need for assessment and explores the role of 3D geospatial information and modeling regarding the indoor structure and navigational routes which can be utilized as disaster risk reduction and response strategy. The reviewed models or methods are analysed testing parameters in the context of indoor risk and disaster management. These parameters are level of detail, connection to outdoor, spatial model and network, handling constraints. 3D reconstruction of indoors requires the structural data to be collected in a feasible manner with sufficient details. Defining the indoor space along with obstacles is important for navigation. Readily available technologies embedded in smartphones allow development of mobile applications for data collection, visualization and navigation enabling access by masses at low cost. The paper concludes with recommendations for 3D modeling, navigation and visualization of data using readily available smartphone technologies, drones as well as advanced robotics for Disaster Management.</p>

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Section: D Data Collection and Reconstructionmentioning

confidence: 99%