Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system

Unmanned Aerial Vehicles (UAVs) as a data acquisition platform and as a measurement instrument are becoming attractive for many surveying applications in civil engineering.

Their performance, however, is not well understood for these particular tasks. The scope of the presented work is the performance evaluation of a UAV system that was built to rapidly and autonomously acquire mobile 3D mapping data.

Details to the components of the UAV system (hardware and control software) are explained. A novel program for photogrammetric flight planning and its execution for the generation of 3D point clouds from digital mobile images is explained.

A performance model for estimating the position error was developed and tested in several realistic construction environments. Test results are presented as they relate to large excavation and earth moving construction sites.

The experiences with the developed UAV system are useful to researchers or practitioners in need for successfully adapting UAV technology for their applications.

Read more:

https://www.researchgate.net/publication/260270622_Mobile_3D_mapping_for_surveying_earthwork_projects_using_an_Unmanned_Aerial_Vehicle_UAV_system

Digital Innovations in European Archaeology

 

European archaeologists in the last two decades have worked to integrate a wide range of emerging digital tools to enhance the recording, analysis, and dissemination of archaeological data.

These techniques have expanded and altered the data collected by archaeologists as well as their interpretations. At the same time archaeologists have expanded the capabilities of using these data on a large scale, across platforms, regions, and time periods, utilising new and existing digital research infrastructures to enhance the scale of data used for archaeological interpretations.

This Element discusses some of the most recent, innovative uses of these techniques in European archaeology at different stages of archaeological work. In addition to providing an overview of some of these techniques, it critically assesses these approaches and outlines the recent challenges to the discipline posed by self-reflexive use of these tools and advocacy for their open use in cultural heritage preservation and public engagement.

Among these techniques used frequently in various archaeological contexts across Europe, aerial photogrammetry, utilising photographs taken by UAVs (Unmanned Aerial Vehicles) has been used to document larger landscapes and close-range photogrammetry is becoming a ubiquitous recording tool on excavations and for historic architectural recording. The low financial entry point to photogrammetry has made it an ideal technique for archaeologists, who are often working on a shoe-string budget.

Most archaeological projects are already equipped with a digital SLR (Single Lens Reflex) camera and most of the necessary software licenses for image processing are open access or available at steeply reduced educational discounts.

Read more: https://www.cambridge.org/core/elements/digital-innovations-in-european-archaeology/BDEA933427350E7D500F773A31EC9F4B/core-reader

Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms

Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the Air Quality Index (AQI).

To this end, this paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting.

Specifically, in the air, this framework leverages a light-weight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by UAVs (Unmanned Aerial Vehicles) for predicting AQI scale distribution.

Furthermore, the Federated Learning Framework not only allows various organizations or institutions to collaboratively learn a well-trained global model to monitor AQI without compromising privacy, but also expands the scope of UAV swarms monitoring.

For ground sensing systems, it is proposed a GC-LSTM (Graph Convolutional neural network-based Long Short-Term Memory) model to achieve accurate, real-time and future AQI inference. The GC-LSTM model utilizes the topological structure of the ground monitoring station to capture the spatio-temporal correlation of historical observation data, which helps the aerial-ground sensing system to achieve accurate AQI inference.

Through extensive case studies on a real-world dataset, numerical results show that the proposed framework can achieve accurate and energy-efficient AQI sensing without compromising the privacy of raw data.

Read more: https://ieeexplore.ieee.org/abstract/document/9184079

Vision-Based Obstacle Avoidance for UAVs via Imitation Learning with Sequential Neural Networks

This paper explores the feasibility of a framework for vision-based obstacle avoidance techniques that can be applied to UAVs (Unmanned Aerial Vehicles) where such decision-making policies are trained upon supervision of actual human flight data.

The neural networks are trained based on aggregated flight data from human experts, learning the implicit policy for visual obstacle avoidance by extracting the necessary features within the image. The images and flight data are collected from a simulated environment provided by Gazebo, and Robot Operating System is used to provide the communication nodes for the framework.

The framework is tested and validated in various environments with respect to four types of neural network including fully connected neural networks, two- and three-dimensional CNNs (Convolutional Neural Networks), and Recurrent Neural Networks (RNNs). Among the networks, sequential neural networks (i.e., 3D-CNNs and RNNs) provide the better performance due to its ability to explicitly consider the dynamic nature of the obstacle avoidance problem.

Read more at: https://link.springer.com/article/10.1007/s42405-020-00254-x

Classification of Grassland Desertification in China Based on Vis-NIR UAV Hyperspectral Remote Sensing

In this study, a vis-NIR (visual Near Infra Red) hyperspectral remote sensing system for UAVs (Unmanned Aerial Vehicles) was used to analyze the type and presence of vegetation and soil of typical desertified grassland in Inner Mongolia using a DBN (Deep Belief Network), 2D CNN (2D Convolutional Neural Network) and 3D CNN (3D Convolutional Neural Network).

The results show that these typical deep learning models can effectively classify hyperspectral data on desertified grassland features. The highest classification accuracy was achieved by 3D CNN, with an overall accuracy of 86.36%. This study enriches the spatial scale of remote sensing research on grassland desertification, and provides a basis for further high-precision statistics and inversion of remote sensing of grassland desertification.

Read more: https://www.spectroscopyonline.com/view/classification-grassland-desertification-china-based-vis-nir-uav-hyperspectral-remote-sensing

Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

Aerial imaging from UAVs (Unmanned Aerial Vehicles) permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest.

However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures.

This study aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D CNN (2D Convolutional Neural Networks) are developed based on transfer learning from two well-known networks: AlexNet and VGGNet.

In contrast, a 3DFCN (3D Fully Convolutional Network) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. 

This demonstrates the value and importance of 3D Datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures.

Read more: https://www.mdpi.com/2504-446X/4/2/24/htm

Application of Remotely Piloted Unmanned Aerial Vehicles in Construction Management

Construction projects may face challenges due to long project duration, uncertainties and big size.

In recent times, remarkable research work has been done on automation of construction.

Unmanned Aerial Vehicles are exponentially being utilized in various civil engineering areas like land surveying, crack detection, construction logistic management, highway asset management and site inspection.

It is always difficult to monitor and track the status of a large construction site. However, unmanned aerial vehicles collect huge data of a construction project quickly.

Remotely located large-scaled construction sites can be monitor by using advanced IT technology in a frequent manner. In this research endeavor, a drone has been used for construction monitoring of the G+6 building with the help of Pix4D software.

This research proposed the unmanned aerial vehicle enabled site to automation BIM (Building Information Modeling). Unmanned aerial vehicle-captured visual data can be utilized effectively with the help of Pix4Dbim. The robotic data collection during construction monitoring can provide enormous benefits to building information modeling.

Read more: https://link.springer.com/chapter/10.1007/978-981-15-5195-6_25

UAV Photogrammetry for topographic monitoring of coastal areas

Coastal areas suffer degradation due to the action of the sea and other natural and human-induced causes.

Topographical changes in beaches and sand dunes need to be assessed, both after severe events and on a regular basis, to build models that can predict the evolution of these natural environments.

This is an important application for airborne Laser Imaging Detection and Ranging (LIDAR) and conventional photogrammetry is also being used for regular monitoring programs of sensitive coastal areas.

This paper analyses the use of UAVs (Unmanned Aerial Vehicles) to map and monitor sand dunes and beaches. A very light plane equipped with a very cheap, non-metric camera was used to acquire images with ground resolutions better than 5 cm.

The Agisoft Photoscan software was used to orientate the images, extract point clouds, build a digital surface model and produce orthoimage mosaics. The processing, which includes automatic aerial triangulation with camera calibration and subsequent model generation, was mostly automated.

To achieve the best positional accuracy for the whole process, signalised ground control points were surveyed with a differential GPS (Ground Positioning System) receiver. Two very sensitive test areas on the Portuguese northwest coast were analysed.

Detailed DSMs were obtained with 10 cm grid spacing and vertical accuracy (RMS) ranging from 3.5 to 5.0 cm, which is very similar to the image ground resolution (3.2–4.5 cm). Where possible to assess, the planimetric accuracy of the orthoimage mosaics was found to be subpixel.

Within the regular coastal monitoring programme being carried out in the region, UAVs can replace many of the conventional flights, with considerable gains in the cost of the data acquisition and without any loss in the quality of topographic and aerial imagery data.

Read more:

https://www.researchgate.net/publication/274645398_UAV_photogrammetry_for_topographic_monitoring_of_coastal_areas

Change Detection in Aerial Images Using Three-Dimensional Feature Maps

Interest in aerial image analysis has increased owing to recent developments in and availability of aerial imaging technologies, like UAVs (Unmanned aerial vehicles), as well as a growing need for autonomous surveillance systems.

Variant illumination, intensity noise, and different viewpoints are among the main challenges to overcome in order to determine changes in aerial images. In this paper, it is presented a robust method for change detection in aerial images.

To accomplish this, the method extracts three-dimensional (3D) features for segmentation of objects above a defined reference surface at each instant. The acquired 3D feature maps, with two measurements, are then used to determine changes in a scene over time.

In addition, the important parameters that affect measurement, such as the camera’s sampling rate, image resolution, the height of the drone, and the pixel’s height information, are investigated through a mathematical model. To exhibit its applicability, the proposed method has been evaluated on aerial images of various real-world locations and the results are promising.

The performance indicates the robustness of the method in addressing the problems of conventional change detection methods, such as intensity differences and shadows.

(Read more)

Unmanned aerial vehicle (UAV) derived SfM photogrammetry point clouds for oil palm canopy segmentation and height estimation

The vast size of oil palm (Elaeis guineensis) plantations has led to lightweight Unmanned Aerial Vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution.

If acquired with sufficient overlap, aerial data from UAVs can be processed within SfM (Structure-from-Motion) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted.

Using lightweight UAVs it has been captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and >75% image overlap.

Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms).

Stem heights could be predicted from point cloud derived metrics with RMSEs (Root-Mean-Square Errors) of 0.27 m (R2 = 0.63) for 7 year old and 0.45 m (R2 = 0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (>75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs.

It is concluded that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.

Read more:

https://www.tandfonline.com/doi/abs/10.1080/01431161.2019.1591651

Global UAVs Market Research 2018-2023

Global Unmanned Aerial Vehicles market report depicts the comprehensive and collaborative analysis of Unmanned Aerial Vehicles industry during past, present and forecast period.

All the industry verticals like competitive market scenario, regional Unmanned Aerial Vehicles presence, and development opportunities are explained. Top players of Unmanned Aerial Vehicles industry, their business tactics and growth opportunities are covered in this report.

Unmanned Aerial Vehicles product portfolio, applications, pricing structures are explained in this report. Initially, the scope of Unmanned Aerial Vehicles industry, definition, classification, objectives and market size estimation is covered.

This study presents a 360-degree market view with statistics and market numbers from 2013-2023. Primary regions analyzed in this report include North America, Europe, Asia-Pacific, Middle East & Africa and South America.

Global Unmanned Aerial Vehicles Industry Top Players Are:

Aeronautics Ltd.

Aerovironment Inc.

Bae Systems Plc

Elbit Systems Ltd.

General Atomics Aeronautical Systems, Inc. (Ga-Asi)

Israel Aerospace Industries Ltd.

Lockheed Martin

Northrop Grumman Corporation

Saab AB

Safran

Textron Inc.

Thales Group

The Boeing Company

Turkish Aerospace Industries, Inc.

Download Free Sample Report Copy @:

https://www.globalmarketers.biz/report/aerospace-&-defense/global-unmanned-aerial-vehicles-industry-market-research-report/2638_request_sample

The methodology of documenting cultural heritage sites using photogrammetry, UAV, and 3D printing techniques: the case study of Asinou Church in Cyprus

As the affordability, reliability and ease-of-use of Unmanned Aerial Vehicles (UAV) advances, the use of aerial surveying for cultural heritage purposes becomes a popular choice, yielding an unprecedented volume of high-resolution, geo-tagged image-sets of historical sites from above.

As well, recent developments in photogrammetry technology provide a simple and cost-effective method of generating relatively accurate 3D models from 2D images. These techniques provide a set of new tools for archaeologists and cultural heritage experts to capture, store, process, share, visualise and annotate 3D models in the field.

This paper focuses on the methodology used to document the cultural heritage site of Asinou Church in Cyprus using various state of the art techniques, such as UAV, photogrammetry and 3D printing. Hundreds of images of the Asinou Church were taken by a UAV with an attached high resolution, low cost camera. These photographic images were then used to create a digital 3D model and a 3D printer was used to create a physical model of the church.

Such a methodology provides archaeologists and cultural heritage experts a simple and cost-effective method of generating relatively accurate 3D models from 2D images of cultural heritage sites.

(Read more...)

Trends in Israeli Military Innovation

It should not be surprising that Israel has become a leader in military innovation given the demands of national security. Among the technologies that it has advanced are Unmanned Aerial Vehicles (UAVs). Even though other nations have conducted experiments with these vehicles, Israel developed and fielded them as battlefield systems.

(Read more)

Structure Analysis and Optimization of Transitioning UAV

With the aim to develop more efficient aircraft configurations, the Blended-Wing-Body (BWB) Unmanned Aerial Vehicles have grown attention in recent years. Compared to conventional aircraft configurations, the BWB structure has several advantages in aerodynamics and fuel efficiency.

Topology Optimization (TO) is also a relatively new structure optimization approach which has applied successfully in automotive industry for a considerable time. In this paper, topology optimization method will be applied on a special BWB structure UAV called BITU on both 2D and 3D models in ABAQUS.

The optimization goal is to minimize compliance energy under specified loading and boundary conditions which will be computed in modeling and simulation section. Finally, optimized result compared to initial design will demonstrate TO is a rational and efficient design tool for structure optimization, especially in Aircraft industry.

ANSYS: New Solutions to develop UAVs 4.0

The Unmanned Aerial Vehicles (UAVs) are rapidly expanding their capabilities beyond military surveillance applications.

UAVs are being developed for parcel delivery to the customer’s doorstep, internet provision, disaster surveillance and assistance, and a whole range of hobbyist activities.

They play a role in the Internet of Things (IoT) because they are critically dependent on sensors, antennas and embedded software to provide two-way communications for remote control and monitoring.

UAV simulation will be critical in achieving industry estimates, which predict that the UAV market could be valued at $82 billion and employ more than 100,000 people by 2025.

To make this business potential a reality, UAV simulation must be performed to ensure that they are safe and reliable with low cost and low maintenance requirements.

They must have control software that is certified for safety, effective communications systems, efficient power management and the ability to operate in challenging environments.

ANSYS can help with UAV simulations by simulating aerodynamics for efficient flight, embedded software for reliable operation, and on-board electronics systems for sensing, power management, flight control, communications, etc.

In addition, ANSYS structural solutions can ensure mechanical integrity for robust and reliable performance. Explore the crucial technologies for designing better drones.

Read More:

http://www.ansys.com/Campaigns/internet-of-things/drones

The Indian Army is interested in purchasing advanced UAS

The Indian army is interested in purchasing advanced UAVs (Unmanned Aerial Vehicles) to strengthen its Intelligence, Surveillance and Reconnaissance (ISR) capabilities and improve the effectiveness of its military operations.

More specifically, the army is waiting for vendors to respond to a Request For Information (RFI) for 60 short-range UAVs that can operate for 10 hours at 15.000 feet, and 120 HALE (High Altitude and Long Endurance) UAVs that can operate for 30 hours at 60.000 feet.

The Indian army’s existing unmanned systems’ fleet comprises Heron MALE (Medium Altitude and Long Endurance) UAVs, and the smaller Searcher Mark II tactical UAVs, both built by IAI (Israel Aerospace Industries).

Oxford University researchers to develop a "Falcon" UAV

Next-generation UAVs (Unmanned Aerial Vehicles) built to track and hunt other UAVs, might be designed using hunting principles used by one of nature’s most capable predators: Researchers at Oxford University have discovered that peregrine falcons steer their attacks using the same control strategies as guided missiles.

The research results could be applied to the design of small, visually guided UAVs that can take down other ‘rogue’ UAVs in settings such as airports or prisons: Recent publicity has revealed the growing problem of UAVs flying drugs and mobile phones into prisons, and of UAVs being flown in the vicinity of airports.

The research was initially funded by the US Air Force Research Laboratory and published open access in the journal PNAS (Proceedings of the National Academy of Sciences). The researchers collected on-board video giving a falcon’s-eye view of the attacks and used this to back up their conclusions. Remarkably, they found that the terminal attack trajectories of peregrines follow the same law –known as PN (Proportional Navigation)– used by visually guided missiles, but with a tuning appropriate to their lower flight speed.

This method does not require any information on a target’s speed or distance, instead relying simply on information about the rotation of the attacker’s line of sight to the target. The researchers conclude that proportional navigation guidance optimised for low flight speeds could find use in small, visually guided UAVs designed to remove other UAVs from protected airspace. The researchers used miniature Global Positioning System (GPS) receivers to track peregrines attacking dummy targets thrown by a falconer or towed by a UAV (Unmanned Aerial Vehicle) and were able to apply a mathematical simulation to these movements describing the dynamics of the guidance system used in intercepting the dummy prey.

Application of Additive Manufacturing for Light-weight UAV Wing Structures

UAVs (Unmanned Aerial Vehicles) have been developed to perform various military and civilian applications.

The present research is motivated by the need to develop a fast adaptable UAV design technologies for agile, fuel efficient, and flexible structures that are capable of adapting and operating in any environments.

The objective of this research is to develop adaptive design technologies by investigating current design methods and knowledge of deployable technologies in the area of engineering design and manufacturing.

More specifically, this research seeks to identify one truss lattice with the optimal elastic performance for deployable UAV wing design according to the Hashin & Shtrikman theoretical bounds.

We propose three lattice designs - 3D Kagome structure, 3D Pyramidal structure and the 3D Hexagonal Diamond structure. The proposed lattice structure designs are fabricated using an Stratasys Objet350 3D Printer while the material chosen is a polypropylene-like photopolymer called Objet DurusWhite RGD430.

Based on compression testing, the proposed inflatable wing design will combine the advantages of compliant mechanisms and deployable structures to maximize flexibilities of movement in UAV design and development.

(Read more...)

Additive manufacturing in UAVs: Challenges and potential

UAVs (Unmanned Aerial Vehicles) are gaining popularity due to their application in military, private and public sector, especially being attractive for fields where human operator is not required.

Light-weight UAVs are more desirable as they have better performance in terms of shorter take-off range and longer flight endurance. However, light weight structures with complex inner features are hard to fabricate using conventional manufacturing methods.

The ability to print complex inner structures directly without the need of a mould gives Additive Manufacturing (AM) an edge over conventional manufacturing. Recent development in composite and multi-material printing opens up new possibilities of printing lightweight structures and novel platforms like flapping wings with ease.

This paper explores the impact of additive manufacturing on aerodynamics, structures and materials used for UAVs. The review will discuss state-ofthe-art

AM technologies for UAVs through innovations in materials and structures and their advantages and limitations. The role of additive manufacturing to improve the performance of UAVs through smart material actuators and multi-functional structures will also be discussed.

(Read More)

¿Can 3D Printing get married with traditional technologies?

More and more, Additive Manufacturing is now seen as a complementary technology, as witnessed by the increased in hybrid printers that combine 3D Printing and CNC machining.

Now, Stratasys, one of the leading players in the 3D printing industry, is sharing some of that expertise via a new whitepaper titled "How Additive and Traditional Manufacturing Mix".

The whitepaper is free to download from 3dprint.com after you fill out a brief form, by clicking here: https://3dprint.com/stratasys-how-additive-and-traditional-manufacturing-mix/.