861 research outputs found
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
Review of UAV positioning in indoor environments and new proposal based on US measurements
Este documento se considera que es una ponencia de congresos en lugar de un capĂtulo de libro.10th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2019) Pisa, Italy, September 30th - October 3rd, 2019The use of unmanned aerial vehicles (UAVs) has increased dramatically in recent years because of their huge potential in both civil and military applications and the decrease in prize of UAVs products. Location detection can be implemented through GNSS technology in outdoor environments, nevertheless its accuracy could be insufficient for some applications. Usability of GNSS in indoor environments is limited due to the signal attenuation as it cross through walls or the absence of line of sight. Considering the big market opportunity of indoor UAVs many researchers are devoting their efforts in the exploration of solutions for their positioning. Indoor UAV applications include location based services (LBS), advertisement, ambient assisted living environments or emergency response.
This work is an update survey in UAV indoor localization, so it can provide a guide and technical comparison perspective of different technologies with their main advantages and drawbacks. Finally, we propose an approach based on an ultrasonic local positioning system.Universidad de AlcaláJunta de Comunidades de Castilla-La ManchaMinisterio de EconomĂa, Industria y Competitivida
Map-Based Localization for Unmanned Aerial Vehicle Navigation
Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments.
Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments.
The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%
Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry
Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a
more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of
meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This
path is already being taken by the recent and fast-developing research in computational fields, however, some
issues related to computationally expensive processes in the integration of multi-source sensing data remain.
Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned
Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope,
many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and
multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant
contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and
hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields
concentrate most applications and are widely studied. Many challenges are currently being overcome by recent
methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image
datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that
are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are
presented.European Commission 1381202-GEU
PYC20-RE-005-UJA
IEG-2021Junta de Andalucia 1381202-GEU
PYC20-RE-005-UJA
IEG-2021Instituto de Estudios GiennesesEuropean CommissionSpanish Government UIDB/04033/2020DATI-Digital Agriculture TechnologiesPortuguese Foundation for Science and Technology 1381202-GEU
FPU19/0010
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