In-flight Accuracy Estimation for Airborne Lidar Data

One of the main problems of today’s Airborne Laser Scanning (ALS) systems is the lack of reliable data quality assessment within or shortly after the airborne survey campaign. This paper presents the development and implementation of a tool that allows complete data quality assessment directly “on the fly”. Its prerequisite is the real-time (RT) GPS/INS processing and subsequent georeferencing of the laser returns. The core of this monitoring tool is a full ALS error propagation engine, yielding the estimation of the expected point cloud accuracy in-flight. The error propagation considers the errors due to the direct georeferencing (DG), the measurement errors of the laser itself (ranging accuracy, encoder errors, etc.) and the variation of the range-finder error due to changing scanning geometry. Unlike the first two error sources, which can be assessed by propagation of the functional relations, the influence of the scanning geometry is much harder to assess, as it requires a-priori knowledge of the local terrain normal and the footprint size. This paper presents the methodology to estimate these parameters directly from the laser point cloud and derive a final quality indicator reflecting the georeferencing quality and the scanning geometry. To predict the accuracy of the point cloud, the tool also features an algorithm predicting the likelihood of fixing the differential carrier-phase ambiguities in postprocessing. Further, the paper discusses the adopted strategy for data processing and communication in order to cope with the constraints imposed by RT processing. We validate the predicted data quality and accuracy estimates by first practical experiences.

Published in:
Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), 434-441
Presented at:
ION GNSS 2009, Savannah, GA, USA, September 22 - 25, 2009
Inst Navigation, 815 15Th Th St Nw, Ste 832, Washington, Dc 20005 Usa

 Record created 2011-05-23, last modified 2018-01-28

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