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Publication . Other literature type . Article . Conference object . 2019


M. Bremer; M. Bremer; V. Wichmann; M. Rutzinger; T. Zieher; J. Pfeiffer;
Open Access
Abstract. In complex mountainous terrain the mapping efficiency is a crucial factor. Unmanned aerial vehicle (UAV) based laser scanning (ULS) has the capability for efficient mapping, as it allows realizing higher flight velocities, higher flying altitude above ground level (AGL) and larger distances between neighbouring flight strips, compared to image based techniques. However, fully utilising the efficiency of the system in mission planning (especially for complex terrain projects, where occlusions and differently inclined surfaces are present) is prone to miss the project requirements in terms of point density and strip overlap. Therefore, the numerical simulation of point densities is a helpful tool for realizing a reliable planning of scan coverage. We implemented a ray-tracing-based ULS-simulator, specifically designed for emulating the mechanism of a Riegl VUX-1LR laser scanner carried by a Riegl RiCOPTER. The simulator can consider copter and scanner motion, which makes it possible to generate synthetic scan data excluding or including the aircraft movement due to aerodynamics by using either planned trajectories from a flight planning software or recorded and post-processed trajectories from an inertial measurement unit (IMU). Laser shots are simulated by intersecting rays from the virtual scanner with a mesh-based digital surface model (DSM). The results show that the tool generates plausible synthetic laser point distributions. However, this is only the case, when aircraft aerodynamics are considered, as the effect of striping due to flight control corrections during the flight is very prominent. It can be shown that applying the presented tool for mission planning (without knowing the actual flight movements) has to consider an error margin of ±50pts/m2 in order to guarantee a compliance with the planned project requirements. Nevertheless, the consideration of terrain by a high resolution DSM, especially in complex terrain, improves the correlation between simulated and real point densities significantly.
Subjects by Vocabulary

ACM Computing Classification System: ComputerApplications_COMPUTERSINOTHERSYSTEMS

Microsoft Academic Graph classification: Laser scanning Aerodynamics Flight planning Real-time computing Terrain Inertial measurement unit Computer science Lidar

Library of Congress Subject Headings: lcsh:Technology lcsh:T lcsh:Engineering (General). Civil engineering (General) lcsh:TA1-2040 lcsh:Applied optics. Photonics lcsh:TA1501-1820

Applanix, 2019. Last accessed: 16 January 2019.

Baltsavias, E. P., 1999. Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry and Remote Sensing 54 (2-3), 199-214.

Kukko, A. and Hyyppa, J., 2009. Small-footprint Laser Scanning Simulator for System Validation, Error Assessment, and Algorithm Development. Photogrammetric Engineering and Remote Sensing 75(10), 1177-1189.

Lohani, B. and Mishra, R. K., 2007. Generating lidar data in laboratory: Lidar simulator. International Archive of Photogrammetry and Remote Sensing XXXVI(3)W52 of Laser Scanning 2007 and SilviLaser 2007, p. 6.

Lovell, J., Jupp, D., Newnham, G., Coops, N. and Culvenor, D., 2005. Simulation study for finding optimal lidar acquisition parameters for forest height retrieval. Forest Ecology and Management 214(1-3), 398-412.

Riegl LMS, 2019. Last accessed: 16 January 2019.

UgCS, 2019. Last accessed: 16 January 2019.

Funded by
OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks
  • Funder: European Commission (EC)
  • Project Code: 776848
  • Funding stream: H2020 | IA
Validated by funder
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