Abstract
Drones are becoming a necessary and invaluable tool in many industries, as well as in emergency response situations. They can assist emergency-services in hazardous situations to get better situational awareness. This may lead to an improved rescue-coordination, increased personal safety for agents in the field and less personal, physical and financial damages as a result of a faster and better intervention. Photo-realistic 3D models generated from the drone video data, for example, can provide situational awareness as it is easier to understand the scene by visualizing it in 3D. The 3D model can be viewed from different perspectives and provides an instant overview of the situation. In contrast to SLAM which is fast but sparse, and SfM-MVS which is dense but slow, we present a pipeline that produces a dense photo-realistic 3D model of the event site in near real time by fusing oblique images with pre-recorded, publicly available LiDAR datasets.
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Coudron, I., Goedemé, T. (2019). Rapid Urban 3D Modeling for Drone-Based Situational Awareness Assistance in Emergency Situations. In: Chen, L., Ben Amor, B., Ghorbel, F. (eds) Representations, Analysis and Recognition of Shape and Motion from Imaging Data. RFMI 2017. Communications in Computer and Information Science, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-030-19816-9_11
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DOI: https://doi.org/10.1007/978-3-030-19816-9_11
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