Drone-Assisted Disaster Management: Finding Victims via Infrared Camera and Lidar Sensor Fusion

Lee, Seoungjun; Har, Dongsoo; Kum, Dongsuk

doi:10.1109/apwc-on-cse.2016.025

Cited by 69 publications

(49 citation statements)

References 12 publications

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“…and (2) to complement space-borne data when the weather is too cloudy. UAVs can then be implemented with imagery and LiDAR systems for safety missions (Lee et al 2016), geological mapping (Jiang et al 2014) and structures and infrastructure surveying (Yamazaki and Liu 2016). For instance, the Chinese authorities used for the first time drones after the magnitude 7.9 May 2008 Sichuan earthquake to assess damages to buildings and infrastructures, or to optimize the deployment of rescue teams by observing the extent of damaged roads and/or traffic jams (https ://www.…”

Section: Immediate Support During Emergencymentioning

confidence: 99%

Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards

et al. 2020

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This article presents a review of the use of unmanned aerial vehicles (UAVs) in the context of geohazards. The pluri-disciplinary role of UAVs is outlined in numerous studies associated with mass earth movements, volcanology, flooding events and earthquakes. Scientific advances and innovations of several research teams around the world are presented from pre-events investigations to crisis management. More particularly, we emphasize the actual status of technology, methodologies and different applications that have emerged with the use of UAVs for each domain. It is shown that the deployment of UAVs in the geohazards context has experienced a tremendous increase during the last 10 years, with the development of more and more miniaturized, flexible and reliable systems. The use of such technology (UAV platform, instrumentation, methodologies) is different for each domain, depending on the spatial extent and the time scale of the observed phenomenon, but also on the practical constraints associated with the civil aviation agencies regulations (outside or within urban areas, before or during a crisis…). This paper also highlights the use of recent methodologies associated with semi-automatic/automatic segmentation or deep learning for the processing of important amounts of data provided by UAVs. Finally, although still sparse, the joint use of UAVs and satellite data is progressing and remains a challenge for future studies in the context of geohazards.

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Section: Immediate Support During Emergencymentioning

confidence: 99%

Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards

et al. 2020

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“…The user can insert the graphical elements in the drone flight environment panel (2), which is used to create the environment in which the tool will simulate drones in an application. This panel consists of a grid layout in which each cell can contain one or more graphical elements.…”

Section: The Dragonfly Toolmentioning

confidence: 99%

“…The use of drones to support different types of applications ranging from search-and-rescue, to goods delivery and surveillance tasks has become a reality [1], [2]. However, it is possible to encounter uncertainties and exceptional situations, not initially predicted, during the use of drone-based applications.…”

Section: Introductionmentioning

confidence: 99%

Dragonfly: a Tool for Simulating Self-Adaptive Drone Behaviours

Maia

Vieira

Chagas

et al. 2019

2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Drone simulators can provide an abstraction of different applications of drones and facilitate reasoning about distinct situations, in order to evaluate the effectiveness of these applications. In this paper we describe Dragonfly, a simulator of the behaviours of individual and collection of drones in various environments, involving random contextual variables and different environmental settings. Dragonfly supports the use of several drones in applications and evaluates the satisfaction of requirements under normal and exceptional situations. It simulates adaptive behaviours of drones due to exceptional situations. The adaption of drones is based on the use of wrappers implemented using aspect-oriented programming.

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“…Note that the robot used in both of the above papers was a ground-based device. Lee et al [85] has used a LiDAR, along with a Virtual Reality (VR) headset, to obtain high resolution positioning using a drone. This experiment was in part inspired by disaster management and designed for first responders as an aid for finding survivors.…”

Section: Lidarsmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Drone-Assisted Disaster Management: Finding Victims via Infrared Camera and Lidar Sensor Fusion

Cited by 69 publications

References 12 publications

Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards

Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards

Dragonfly: a Tool for Simulating Self-Adaptive Drone Behaviours

Sensor Modalities and Fusion for Robust Indoor Localisation

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