Comparison of 6 Common Noise Filter Methods for Terrestrial Laser Scanner Target

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Abstract:

The terrestrial laser scanner (TLS) is a line-of-sight instrument which can directly acquire dense 3D point clouds in a very short time. The registration method is used to act as a control point for a reflective target placed in overlapping areas in neighboring scanning stations. The main purpose for a reflective target is to be an obvious and precise positioning feature for laser scanning point cloud data. This study compares six common noise filtering methods which are used to eliminate measuring noise in the primitive point cloud of reflective target for better positioning accuracy, i.e. Scatter points removal method, Spikes removal method, Least squares plane method, Gaussian smoothing method, Median smoothing method, and the Average smoothing method. The test results found for 30, 60, and 90 meter distances in this study was that six noise filtering methods can actually increase the reflective target positioning accuracy, and the least squares plane method is the best one in either precision or accuracy. Beside the reflective target, the test is carried out with self-made targets for the comparison of positioning accuracy between the reflective target and self-made targets. Regarding accuracy, the reflective target is superior to the object in 30 and 60m, and self-made target is superior to the reflective target in 90m.

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2929-2934

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May 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Besl, P.J. and N.D. McKay, 1992. A method for registration of 3D shape, IEEE Trans. Pattern Analysis and Machine Intelligence, 14(2):239-256.

DOI: 10.1109/34.121791

Google Scholar

[2] Gruen, A. and D. Akca, 2004. Least Squares 3D Surface Matching, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 34, Part 5/W16.

DOI: 10.1016/j.isprsjprs.2005.02.006

Google Scholar

[3] Akca, D., 2003. Full Automatic Registration of Laser Scanner Point Clouds, Optical 3D Measurement Techniques VI, pp.330-337.

Google Scholar

[4] Derek D. Lichti, Stuart J. Gordon, and Taravudh Tipdecho. Error Models and Propagation in Directly Georeferenced Terrestrial Laser Scanner Networks, Journal of Surveying Engineering, 2005.

DOI: 10.1061/(asce)0733-9453(2005)131:4(135)

Google Scholar