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Evaluation of Low Cost Laser Scanner for Rapid Registration of Point Clouds with Limited Information on Rotation and Relative Location

Shakhzod TakhirovDepartment of Civil and Environmental Engineering, Univerity of California-Berkeley, Berkeley, USASultan KudratovDepartment of Information Technologies, Tashkent University of Information Technologies, Tashkent, Uzbekistan
ABI

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Laser scanners are becoming more affordable. The cost of a laser scanner has been driven down by the high demand for range measuring devices used in autonomous cars. The objective of this paper is to evaluate usage of a cost effective laser scanner for rapid mapping of a building's interior. One of the valuable applications of such devices is for quick structural assessments during reconnaissance efforts right after an earthquake, when measuring a plan of the interior of the building is beneficial for further analysis. Due to safety and the large number of buildings under investigation, researchers have a very limited amount of time to remain inside of the building and get the critical measurements. Having a tool that can create layouts of the interior while a person is moving inside would be beneficial for applications of this kind. For mass application, cost of the device is an important factor. The scanner used in this application creates a plane point cloud. In a horizontal orientation, it creates a horizontal slice of the building's interior that can capture the locations of the openings (doors and windows) and the general dimensions of the room. When the stitching of the laser scans is done on the fly, the floor plan of a large building can be measured with adequate accuracy in a short period of time. The point clouds registered in real-time and transferred over a network have a strong potential to become a very important component in the Internet of Things, a system of interrelated computing devices, mechanical and digital machines, and sensors. This work assumes that information about the orientation of the scanner and its relative location is very limited and as such, the registration of the point clouds is based on the use of common features.

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