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Effective Reduction of Horizontal Error in Laser Scanning Information by Strip‐Wise Least Squares Adjustments
Author(s) -
Lee Byoung Kil,
Yu Kiyun,
Pyeon Moowook
Publication year - 2003
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.03.0102.0212
Subject(s) - strips , mean squared error , inertial measurement unit , laser scanning , mathematics , set (abstract data type) , point (geometry) , reduction (mathematics) , affine transformation , least squares function approximation , approximation error , algorithm , laser , computer science , artificial intelligence , statistics , geometry , optics , physics , estimator , programming language
Though the airborne laser scanning (ALS) technique is becoming more popular in many applications, horizontal accuracy of points scanned by the ALS is not yet satisfactory when compared with the accuracy achieved for vertical positions. One of the major reasons is the drift that occurs in the inertial measurement unit (IMU) during the scanning. This paper presents an algorithm that adjusts for the error that is introduced mainly by the drift of the IMU that renders systematic differences between strips on the same area. For this, we set up an observation equation for strip‐wise adjustments and completed it with tie point and control point coordinates derived from the scanned strips and information from aerial photos. To effectively capture the tie points, we developed a set of procedures that constructs a digital surface model (DSM) with breaklines and then performed feature‐based matching on strips resulting in a set of reliable tie points. Solving the observation equations by the least squares method produced a set of affine transformation equations with 6 parameters that we used to transform the strips for adjusting the horizontal error. Experimental results after evaluation of the accuracy showed a root mean squared error (RMSE) of the adjusted strip points of 0.27 m, which is significant considering the RMSE before adjustment was 0.77 m.

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