UAV-based 3D modelling of disaster scenes for Urban Search and Rescue

Natural or man-made disasters often result in trapped victims under rubble piles. In such emergency response situations, Urban Search and Rescue (USaR) teams have to make quick decisions to determine the location of possible trapped humans. The fast 3D modelling of collapsed buildings using images from Unmanned Aerial Vehicles (UAVs) can significantly help the USaR operations and improve disaster response. The apriori establishment of a proper workflow for fast and reliable image-based 3D modelling and the careful parameterization in every step of the photogrammetric process are crucial aspects that ensure the readiness in an emergency situation. This paper evaluates powerful commercial and open-source software for the creation of 3D models of disaster scenes using UAV imagery for rapid response situations and conducts a thorough analysis on the parameters of the various modelling steps that may lead to the desired results for USaR operations. The main result of our analysis is the establishment of optimized photogrammetric procedures with the scope of fast 3D modelling of disaster scenes, to assist USaR teams and increase survival rates.

[1]  Bernhard Rinner,et al.  Fast aerial image acquisition and mosaicking for emergency response operations by collaborative UAVs , 2011, ISCRAM.

[2]  Carol J. Friedland,et al.  A SURVEY OF UNMANNED AERIAL VEHICLE ( UAV ) USAGE FOR IMAGERY , 2011 .

[3]  F. Nex,et al.  UAV for 3D mapping applications: a review , 2014 .

[4]  Sophia Karagiorgou,et al.  A service oriented architecture for decision support systems in environmental crisis management , 2012, Future Gener. Comput. Syst..

[5]  Norman Kerle,et al.  UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning , 2014 .

[6]  Alexander Ferworn,et al.  Disaster scene reconstruction: modeling and simulating urban building collapse rubble within a game engine , 2013, SummerSim.

[7]  Alexander Ferworn,et al.  Initial experiments on 3D modeling of complex disaster environments using unmanned aerial vehicles , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[8]  G. Lewis,et al.  Evaluating the Use of a Low-Cost Unmanned Aerial Vehicle Platform in Acquiring Digital Imagery for Emergency Response , 2007 .

[9]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[10]  Wesley M. DeBusk.  Unmanned Aerial Vehicle Systems for Disaster Relief: Tornado Alley , 2009 .

[11]  Filiberto Chiabrando,et al.  UAV Deployment Exercise for Mapping Purposes: Evaluation of Emergency Response Applications , 2015, Sensors.

[12]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[13]  Georgios Miaoulis,et al.  Personalised 3D navigation and understanding of Geo-referenced Scenes , 2013, 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[14]  George Athanasiou,et al.  INACHUS : Integrated wide area situation awarenesss and survivor localization in search and rescue operations , 2015 .

[15]  Georgios Miaoulis,et al.  Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes , 2013, GeoInformatica.

[16]  Fumio Yamazaki,et al.  Construction of 3D models of buildings damaged by earthquakes using UAV aerial images , 2015 .

[17]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.