Obstacle Avoidance for Flight Safety on Unmanned Aerial Vehicles

Aguilar, Wilbert G.; Casaliglla, Veronica P.; Polit, Jose L.; Abad, Vanessa; Ruiz, Hugo

doi:10.1007/978-3-319-59147-6_49

Cited by 17 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides NBV, object recognition utilizes machine learning in a posteriori improvements to UAV photogrammetry. Martin et al [36] incorporates anomaly detection and Aguilar et al [69] stresses the importance of obstacle recognition and avoidance. Another important aspect of object recognition, as pertains to UAV photogrammetry, is in-flight object recognition, but in-flight object recognition falls outside the scope of this case study in favor of a posteriori object recognition.…”

Section: A Posteriori Informationmentioning

confidence: 99%

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

Berrett

Vernon

Beckstrand

et al. 2021

Drones

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic COVID-19 pandemic during a mandatory and rare campus closure and details a large scale modeling workflow and best practice data acquisition and processing techniques. The model utilizes 80,384 images and high accuracy GPS surveying points to create a 1.65 trillion-pixel textured structure-from-motion (SfM) model with an average ground sampling distance (GSD) near structures of 0.5 cm and maximum of 4 cm. Separate model segments (31) taken from data gathered between April and August 2020 are combined into one cohesive final model with an average absolute error of 3.3 cm and a full model absolute error of <1 cm (relative accuracies from 0.25 cm to 1.03 cm). Optimized and automated UAV techniques complement the data acquisition of the large-scale model, and opportunities are explored to archive as-is building and campus information to enable historical building preservation, facility maintenance, campus planning, public outreach, 3D-printed miniatures, and the possibility of education through virtual reality (VR) and augmented reality (AR) tours.

show abstract

Section: A Posteriori Informationmentioning

confidence: 99%