Drones as an Integral Part of Remote Sensing Technologies to Help Missing People

Pensieri, Maria Gaia; Garau, Mauro; Barone, Pier Matteo

doi:10.3390/drones4020015

Cited by 31 publications

(16 citation statements)

References 16 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent advances in autonomy of unmanned aerial vehicles (UAVs)-or droneshave increased their adoption in time-critical applications, such as disaster monitoring, surveillance, and SAR [3][4][5][6]. Compared to manned aircraft, UAVs offer high versatility to scout areas due to their reduced size and cost, extensive payload adaptability, and ease of piloting by incorporating automated tasks, including waypoint navigation, obstacle avoidance, and autonomous take-off and landing [7][8][9]. In time-critical applications, such as SAR, real-time camera streaming is critical for UAV operators to understand the situation context and manoeuvre the aircraft during the mission [10].…”

Section: Introductionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

View full text Add to dashboard Cite

Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.

show abstract

Section: Introductionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Unmanned aerial vehicles (UAVs) are widely applied for monitoring and mapping purposes all around the world [1][2][3][4]. The use of relatively low-cost commercial UAV platforms can produce high-resolution visible orthophotos, thus providing an increased resolution product in comparison to the traditional aerial or satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Αγαπίου

2020

Drones

View full text Add to dashboard Cite

Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.

show abstract

“…In order to collect roof data and obtain roof information, in some cases it is essential to use UAVs that allow us to carry out a morphological analysis of the urban landscape, obtaining another second SfM model on a smaller scale [ 30 , 31 ].…”

Section: Developed Methodologymentioning

confidence: 99%

Protocol Development for Point Clouds, Triangulated Meshes and Parametric Model Acquisition and Integration in an HBIM Workflow for Change Control and Management in a UNESCO’s World Heritage Site

Plata

Franco

Franco³

et al. 2021

Sensors

View full text Add to dashboard Cite

This article illustrates a data acquisition methodological process based on Structure from Motion (SfM) processing confronted with terrestrial laser scanner (TLS) and integrated into a Historic Building Information Model (HBIM) for architectural Heritage’s management. This process was developed for the documentation of Cáceres’ Almohad wall bordering areas, a UNESCO World Heritage Site. The case study’s aim was the analysis, management and control of a large urban area where the urban growth had absorbed the wall, making it physically inaccessible. The methodology applied was the combination of: clouds and meshes obtained by SfM; with images acquired from Unmanned Aerial Vehicle (UAV) and Single Lens Reflex (SLR) and terrestrial photogrammetry; and finally, with clouds obtained by TLS. The outcome was a smart-high-quality three-dimensional study model of the inaccessible urban area. The final result was two-fold. On one side, there was a methodological result, a low cost and accurate smart work procedure to obtain a three-dimensional parametric HBIM model that integrates models obtained by remote sensing. On the other side, a patrimonial result involved the discovery of a XII century wall’s section, that had supposedly been lost, that was hidden among the residential buildings. The article covers the survey campaign carried out by the research team and the techniques applied.

show abstract

Drones as an Integral Part of Remote Sensing Technologies to Help Missing People

Cited by 31 publications

References 16 publications

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Protocol Development for Point Clouds, Triangulated Meshes and Parametric Model Acquisition and Integration in an HBIM Workflow for Change Control and Management in a UNESCO’s World Heritage Site

Contact Info

Product

Resources

About