Browse Publications Technical Papers 2020-28-0515
2020-09-25

Autonomous Quadcopter for Building Construction Monitoring 2020-28-0515

Feasibility in Manufacturing of autonomous unmanned aerial vehicles at low cost allows the UAV developers to bring it out with numerous applications for society. Civil domain is a widely developing platform which initiated the development of UAV for civilian applications like bridge inspection, building monitoring, life or strength estimation of historical places and also outdoor and indoor mapping of buildings. These autonomous UAVs with high resolution camera fly over and around the construction sites, buildings, mines and captures images of various locations and point clouds in all sides of the building and creates a 3D map by using photogrammetry techniques. The software auto generates the report and updates it to the cloud which can be accessed online. Autonomous operations are quite difficult in new environments which requires SLAM (simultaneous localization and mapping) to operate the UAV between open spaces. This paper describes the technique of mapping a construction site using a quadcopter and determine the completion of such constructions using image processing and machine learning techniques. Obstacle avoidance during the autonomous flight using ultrasonic sensors provide greater flexibility of the vehicle to move around the buildings.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Members save up to 16% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:
TECHNICAL PAPER

New Paradigm in Robust Infrastructure Scalability for Autonomous Applications

2019-01-0495

View Details

TECHNICAL PAPER

Toward a Framework for Highly Automated Vehicle Safety Validation

2018-01-1071

View Details

TECHNICAL PAPER

Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

2020-01-0739

View Details

X