Three-Dimensional Modeling and Indoor Positioning for Urban Emergency Response

Abstract

Three-dimensional modeling of building environments and indoor positioning is essential for emergency response in cities. Traditional ground-based measurement methods, such as geodetic astronomy, total stations, and global positioning system (GPS) receivers, cannot meet the demand for high precision positioning and it is therefore essential to conduct multiple-angle data-acquisition and establish three-dimensional spatial models. In this paper, a rapid modeling technology is introduced, which includes multiple-angle remote sensing image acquisition based on unmanned aerial vehicles (UAVs), an algorithm to remove linear and planar foregrounds before reconstructing the backgrounds, and a three-dimensional modeling (3DM) framework. Additionally, an indoor 3DM technology is introduced based on building design drawings, and an indoor positioning technology is developed using iBeacon technology. Finally, a prototype system of the indoor and outdoor positioning-service system in an urban firefighting rescue scenario is introduced to demonstrate the value of the method proposed in this paper.

The Use of Decision Support in Search and Rescue: A Systematic Literature Review

“…The supervised learning approach is defined as a class of systems that classifies data and predicts outcomes using labeled datasets for training algorithms [8]. In our study, researchers mostly use supervised learning in applications involving UAVs [38,46]. In unsupervised learning, algorithms are trained using unlabeled datasets.…”

“…e.g., floods, hurricanes, earthquakes, avalanches, and bushfires. Human-made disasters [15,38,46,61,62,[65][66][67]69,70,74,85,86,[93][94][95]97] The articles in which the category of disaster involves the calamities caused by mankind. For instance, fire emergencies such as fire in a building or urban fire emergencies, and day-to-day emergencies that can be traffic accidents, and industrial accidents.…”

“…Nunavath and Prinz [65], Kabir et al [82], and Toujani et al [84] all addressed fire emergencies using NoSQL. Kumar et al [36] and Zhang et al [46] presented data acquisition based on UAVs for emergency response and recovery.…”

A Comparison of Low-Cost Sensor Systems in Automatic Cloud-Based Indoor 3D Modeling

“…The extraction of geometric information from images has been conducted with a multitude of methods, including a combination of spatial layout estimation and pose tracking [25] and the use of textural-based features [26]. A recent increase in studies concerning 3D depth estimation from a single image has been noted [27], where neural networks are used for determining the presence and position of planes based on known ground truth, both for rectangular [28] and panoramic images [29].…”

Introduction to the Special Issue: “State-of-the-Art Virtual/Augmented Reality and 3D Modeling Techniques for Virtual Urban Geographic Experiments”

“…VUGEs relies on 3D modeling for constructing virtual environments; Construction of coupled indoor/outdoor VGEs is particularly challenging. Zhang et al proposed to integrate unmanned aerial vehicle (UAV)-based outdoor 3D reconstruction, computer-aided design (CAD)-based indoor modeling and iBeacon-based indoor positioning in this regard and presented a prototype indoor-outdoor VGE to support urban emergency response [8]. Additionally, new methods for the extraction of urban features from LiDAR point clouds were explored in two studies [9,10].…”