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ABSTRACT:In this contribution it is shown how an UAV system can be built at low costs. The components of the system, the equipment as well as the control software are presented. Furthermore an implemented programme for photogrammetric flight planning and its execution are described. The main focus of this contribution is on the generation of 3D point clouds from digital imagery. For this web services and free software solutions are presented which automatically generate 3D point clouds from arbitrary image configurations. Possibilities of georeferencing are described whereas the achieved accuracy has been determined. The presented workflow is finally used for the acquisition of 3D geodata. On the example of a landfill survey it is shown that marketable products can be derived using a low-cost UAV.

Recent advances in stochastic modelling of reflectorless rangefinders revealed an inherent relationship among raw intensity values and the corresponding precision of observed distances. In order to derive the stochastic properties of a terrestrial laser scanner’s (TLS) rangefinder, distances have to be observed repeatedly. For this, the TLS of interest has to be operated in the so-called 1D-mode—a functionality which is offered only by a few manufacturers due to laser safety regulations. The article at hand proposes two methodologies to compute intensity-based stochastic models based on capturing geometric primitives in form of planar shapes utilising 3D-point clouds. At first the procedures are applied to a phase-based Zoller + Fröhlich IMAGER 5006h. The generated results are then evaluated by comparing the outcome to the parameters of a stochastic model which has been derived by means of measurements captured in 1D-mode. Another open research question is if intensity-based stochastic models are applicable for other rangefinder types. Therefore, one of the suggested procedures is applied to a Riegl VZ-400i impulse scanner, as well as a Leica ScanStation P40 TLS that deploys a hybrid rangefinder technology. The generated results successfully demonstrate alternative methods for the computation of intensity-based stochastic models as well as their transferability to other rangefinder technologies.