The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1385–1390, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1385-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1385–1390, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1385-2020

  22 Aug 2020

22 Aug 2020

VALIDATION OF LIDAR SURVEY DATA BY COMPARISON OF SEVERAL UNCERTAINTY MODELS

N. Seube N. Seube
  • Geown France, 13 avenue de l’Europe, 31520 Ramonville St-Agne, France

Keywords: LiDAR point clouds, Combined Measurement Uncertainty Model, Precision analysis, LiDAR data quality analysis, LiDAR survey data validation

Abstract. This paper introduce a new method for validating the precision of an airborne or a mobile LiDAR data set. The proposed method is based on the knowledge of an a Combined Standard Measurement Uncertainty (CSMU) model which describes LiDAR point covariance matrix and thus uncertainty ellipsoid. The model we consider includes timing errors and most importantly the incidence of the LiDAR beam. After describing the relationship between the beam incidence and other variable uncertainty (especially attitude uncertainty), we show that we can construct a CSMU model giving the covariance of each oint as a function of the relative geometry between the LiDAR beam and the point normal. The validation method we propose consist in comparing the CSMU model (predictive a priori uncertainty) t the Standard Deviation Alog the Surface Normal (SDASN), for all set of quasi planr segments of the point cloud. Whenever the a posteriori (i.e; observed by the SDASN) level of uncertainty is greater than a priori (i.e; expected) level of uncertainty, the point fails the validation test. We illustrate this approach on a dataset acquired by a Microdrones mdLiDAR1000 system.