Real-Time LiDAR-based Power Lines Detection for Unmanned Aerial Vehicles

The growing dependence of modern-day societies on electricity leads to the increasing importance of effective monitoring and maintenance of power lines. Due to the population’s renouncement to the installation of new electric power lines, the existing ones are constantly operating at maximum capacity. This leaves no room for breakdowns, as it leads to major economic losses for the electrical companies and blackouts for the consumers. Endowing Unmanned Aerial Vehicles (UAVs) with the appropriate sensors for inspection the power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual detection methods are usually applied to locate the power lines and their components. Although, they are generally too sensitive to atmospheric conditions and noisy background. Poor light conditions or a background rich in edges may compromise their results. In order to overcome those limitations, this dissertation addresses the problem of power line detection and modeling based on the use of a Light Detection And Ranging (LiDAR) sensor. A novel approach to the power line detection was developed, the Power Line LiDARbased Detection and Modeling (PL2DM). It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. In the segmentation, the breaking cluster points are detected by an analysis of their planar properties. Exporting the potential power line points to a further step, it performs a scan based straight line detection. The final model of the power line is obtained by matching and grouping the several line segments detected using their collinearity properties. Horizontally, the power lines are modeled as a straight line, while vertically are approximated to a catenary curve. The algorithm was tested with a real dataset, showing promising results both in terms of outputs and processing time. From there, it was demonstrated that the proposed algorithm can be applied to real-time operations of the UAV, adding object-based perception capabilities for other layers of processing.A crescente dependência das sociedades modernas no uso de eletricidade conduz a uma crescente importância da eficiência da monitorização e manutenção das linhas elétricas. A renitência das populações `a instalação de novas linhas elétricas faz com que as existentes estejam constantemente a operar na sua máxima capacidade. Isto faz com que não possam existir falhas, uma vez que resultariam em grandes perdas económicas para as companhias elétricas e em falhas energéticas para os consumidores. Equipando um Unmanned Aerial Vehicle (UAV) com os sensores adequados `a inspeção de linhas elétricas, podem ser reduzidos os custos e riscos de operação associados `as inspeções tradicionais, baseadas em patrulhas pedonais e no uso de um helicóptero. No entanto, isto implica o desenvolvimento de algoritmos para que o processo de inspeção seja fiável e autónomo. As linhas elétricas e os componentes associados são geralmente localizados através de métodos de deteção visual. Estes m´métodos são, geralmente, muito sensíveis `as condições atmosféricas e a fundos ruidosos. Condições de luz deficientes ou fundos ricos em contrastes são alguns dos fatores que podem comprometer os seus resultados. De forma a ultrapassar essas limitações, esta dissertação endereça o problema da deteção e modelação de linhas elétricas, tendo por base o uso de um sensor Light Detection And Ranging (LiDAR). Foi desenvolvida uma nova abordagem aos métodos de deteção de linhas elétricas, o Power Line LiDAR-based Detection and Modeling (PL2DM). Esta abordagem ´e baseada numa análise individual de varrimentos, em que ´e feita uma comparação minimalista de todos os pontos, presentes numa dada nuvem de pontos, com uma vizinhança adaptativa. Na segmentação, os pontos de quebra dos grupos criados são detetados tendo em conta as suas propriedades planares. Passando os pontos passíveis de pertencerem a linhas elétricas para o processamento seguinte, é realizada, em cada varrimento, uma deteção de linhas retas. O modelo final das linhas elétricas é obtido a partir da associação e agrupamento dos diversos segmentos de reta detetados, tendo por base a sua colinearidade. Na sua projeção horizontal, as linhas elétricas são modeladas como linhas retas. Verticalmente, são aproximadas ao modelo de uma curva catenária. O algoritmo foi testado com um conjunto de dados reais, tendo mostrado resultados promissores, tanto em termos de dados gerados como de tempo de processamento. Com isso, ficou demonstrado que o algoritmo proposto pode ser aplicado nas operações do UAV em tempo real, adicionando capacidades de perceção baseada em objetos para outras camadas de processamento

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure Purpose

In this chapter we present the development of automated visual inspection systems for electrical infrastructure. The inspection is performed using images acquired with an unmanned aerial vehicle (UAV). Through automated inspection routes, the state of the infrastructure can be evaluated and then the appropriate correcting measures be taken. The monitoring of power lines can be done using passive sensors such as cameras or active sensors such as light detection and ranging (LIDAR) cameras, image processing techniques, computer vision and control systems can then be used. Additionally, a three-dimensional (3D) reconstruction process is possible using images either offline or during the monitoring. An UAV with an onboard embedded computer is used to execute the computer vision and path planning algorithms. The work done shows that the proposed strategy aids in the automation of power line inspection

MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems

This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot Systems (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity with respect to changes in actuators, frames, and sensory configuration. As the name suggests, the platform is specially tailored for deployment within a MRS group. The MRS Drone contributes to the state-of-the-art of UAV platforms by allowing smooth real-world deployment of multiple aerial robots, as well as by outperforming other platforms with its modularity. For real-world multi-robot deployment in various applications, the platform is easy to both assemble and modify. Moreover, it is accompanied by a realistic simulator to enable safe pre-flight testing and a smooth transition to complex real-world experiments. In this manuscript, we present mechanical and electrical designs, software architecture, and technical specifications to build a fully autonomous multi UAV system. Finally, we demonstrate the full capabilities and the unique modularity of the MRS Drone in various real-world applications that required a diverse range of platform configurations.Comment: 49 pages, 39 figures, accepted for publication to the Journal of Intelligent & Robotic System

LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid

The demand for reliable obstacle warning and avoidance capabilities to ensure safe low-level flight operations has led to the development of various practical systems suitable for fixed and rotary wing aircraft. State-of-the-art Light Detection and Ranging (LIDAR) technology employing eye-safe laser sources, advanced electro-optics and mechanical beam-steering components delivers the highest angular resolution and accuracy performances in a wide range of operational conditions. LIDAR Obstacle Warning and Avoidance System (LOWAS) is thus becoming a mature technology with several potential applications to manned and unmanned aircraft. This paper addresses specifically its employment in Unmanned Aircraft Systems (UAS) Sense-and-Avoid (SAA). Small-to-medium size Unmanned Aerial Vehicles (UAVs) are particularly targeted since they are very frequently operated in proximity of the ground and the possibility of a collision is further aggravated by the very limited see-and-avoid capabilities of the remote pilot. After a brief description of the system architecture, mathematical models and algorithms for avoidance trajectory generation are provided. Key aspects of the Human Machine Interface and Interaction (HMI2) design for the UAS obstacle avoidance system are also addressed. Additionally, a comprehensive simulation case study of the avoidance trajectory generation algorithms is presented. It is concluded that LOWAS obstacle detection and trajectory optimisation algorithms can ensure a safe avoidance of all classes of obstacles (i.e., wire, extended and point objects) in a wide range of weather and geometric conditions, providing a pathway for possible integration of this technology into future UAS SAA architectures

Detecting Invasive Insects with Unmanned Aerial Vehicles

A key aspect to controlling and reducing the effects invasive insect species have on agriculture is to obtain knowledge about the migration patterns of these species. Current state-of-the-art methods of studying these migration patterns involve a mark-release-recapture technique, in which insects are released after being marked and researchers attempt to recapture them later. However, this approach involves a human researcher manually searching for these insects in large fields and results in very low recapture rates. In this paper, we propose an automated system for detecting released insects using an unmanned aerial vehicle. This system utilizes ultraviolet lighting technology, digital cameras, and lightweight computer vision algorithms to more quickly and accurately detect insects compared to the current state of the art. The efficiency and accuracy that this system provides will allow for a more comprehensive understanding of invasive insect species migration patterns. Our experimental results demonstrate that our system can detect real target insects in field conditions with high precision and recall rates.Comment: IEEE ICRA 2019. 7 page