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Open Access Rigorous Strip Adjustment of UAV-based Laserscanning Data Including Time-Dependent Correction of Trajectory Errors

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A new generation of laser scanners mounted on Unmanned Aerial Vehicles (UAVs) have the potential to provide high-quality point clouds of comparatively small areas (a few hectares). The high maneuverability of the UAVs, a typically large field of view of the laser scanners, and a comparatively small measurement range lead to point clouds with very high point density, less occlusions, and low measurement noise. However, due to the limited payload of UAVs, lightweight navigation sensors with a moderate level of accuracy are used to estimate the platform's trajectory. As a consequence, the georeferencing quality of the point clouds is usually sub-optimal; for this, strip adjustment can be performed. The main goal of strip adjustment is to simultaneously optimize the relative and absolute orientation of the strip-wise collected point clouds. This is done by fully re-calibrating the laser scanning system and by correcting systematic measurement errors of the trajectory. In this paper, we extend our previous work on the topic of strip adjustment by the estimation of time-dependent trajectory errors. The errors are thereby modelled by natural cubic splines with constant segment length in time domain. First results confirm the suitability of this flexible correction model by reducing the relative and absolute strip discrepancies to 1.38 cm and 1.65 cm, respectively.
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Document Type: Research Article

Publication date: 01 December 2016

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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