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

Utilizing Airborne LiDAR and UAV Photogrammetry Techniques in Local Geoid Model Determination and Validation

This investigation evaluates the performance of Digital Terrain Models (DTMs) generated in different vertical datums by aerial LiDAR and UAV (Unmanned Aerial Vehicle) photogrammetry techniques, for the determination and validation of local geoid models.

Many engineering projects require the point heights referring to a physical surface, i.e., geoid, rather than an ellipsoid. When a high-accuracy local geoid model is available in the study area, the physical heights are practically obtained with the transformation of Global Navigation Satellite System (GNSS) ellipsoidal heights of the points.

Besides the commonly used geodetic methods, this study introduces a novel approach for the determination and validation of the local geoid surface models using photogrammetry. The numeric tests were carried out in the Bergama region, in the west of Turkey. Using direct georeferenced airborne LiDAR and indirect georeferenced UAV photogrammetry-derived point clouds, DTMs were generated in ellipsoidal and geoidal vertical datums, respectively.

After this, the local geoid models were calculated as differences between the generated DTMs. Generated local geoid models in the grid and pointwise formats were tested and compared with the regional gravimetric geoid model (TG03) and a high-resolution global geoid model (EIGEN6C4), respectively. In conclusion, the applied approach provided sufficient performance for modeling and validating the geoid heights with centimeter-level accuracy. 

Read more at https://www.researchgate.net/publication/344146054_Utilizing_Airborne_LiDAR_and_UAV_Photogrammetry_Techniques_in_Local_Geoid_Model_Determination_and_Validation

Attention-Based Road Registration for GPS-Denied UAS Navigation

Matching and registration between aerial images and prestored road landmarks are critical techniques to enhance UAS (Unmanned Aerial System) navigation in the Global Positioning System (GPS)-denied urban environments.

Current registration processes typically consist of two separate stages of road extraction and road registration. These two-stage registration approaches are time-consuming and less robust to noise.

To that end, it has been investigated the problem of end-to-end Aerial-Road registration. Using deep learning, it has been developed a novel attention-based neural network architecture for Aerial-Road registration.

In this model, it has been constructed two-branch neural networks with shared weights to map two input images into a common embedding space. Besides, considering that road features are sparsely distributed in images, it has been incorporated a novel multibranch attention module to filter out false descriptor matches from the indiscriminative background in order to improve registration accuracy.

Finally, the results from extensive experiments show that compared with state-of-the-art approaches, the mean absolute errors of the approach in rotation angle and the translations in the x- and y-directions are reduced down by a factor of 1.24, 1.38, and 1.44, respectively. Furthermore, as a byproduct, the experimental results prove the feasibility of a neural network multitask learning approach to simultaneously achieve accurate Aerial-Road matching and registration, thus providing an efficient and accurate UAS geolocalization.

More info:

https://ieeexplore.ieee.org/document/9184263

Pronóstico mundial para 2025: adopción cada vez mayor de la industria 4.0 y del IIoT

Research and Markets acaba de lanzar al mercado un interesante informe titulado "Mercado de sensores láser: crecimiento, tendencias, pronósticos (2020-2025)". Según el informe, el mercado global de sensores láser alcanzó un valor superior a los 900 millones de dólares durante el pasado año 2019, y se estima que alcanzará un valor superior a los 1.500 millones de dólares para el año 2025. Entre otros factores que van a potenciar este crecimiento destaca la implementación de las tecnologías industriales englobadas en el concepto de Industria 4.0 y la evolución de la fabricación tradicional hacia una fabricación basada en el IIoT (Industrial IoT) (Industrial Internet of Things)

Más información:

https://www.prnewswire.com/news-releases/global-laser-sensors-industry-forecast-to-2025---increasing-adoption-of-industry-4-0-and-smart-manufacturing-practices-301121888.html