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Potential of ILRIS3D Intensity Data for Planar Surfaces Segmentation
Department of Geomatics, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan
* Author to whom correspondence should be addressed.
Received: 27 April 2009; in revised form: 5 June 2009 / Accepted: 15 July 2009 / Published: 20 July 2009
Abstract: Intensity value based point cloud segmentation has received less attention because the intensity value of the terrestrial laser scanner is usually altered by receiving optics/hardware or the internal propriety software, which is unavailable to the end user. We offer a solution by assuming the terrestrial laser scanners are stable and the behavior of the intensity value can be characterized. Then, it is possible to use the intensity value for segmentation by observing its behavior, i.e., intensity value variation, pattern and presence of location of intensity values, etc. In this study, experiment results for characterizing the intensity data of planar surfaces collected by ILRIS3D, a terrestrial laser scanner, are reported. Two intensity formats, grey and raw, are employed by ILRIS3D. It is found from the experiment results that the grey intensity has less variation; hence it is preferable for point cloud segmentation. A warm-up time of approximate 1.5 hours is suggested for more stable intensity data. A segmentation method based on the visual cues of the intensity images sequence, which contains consecutive intensity images, is proposed in order to segment the 3D laser points of ILRIS3D. This method is unique to ILRIS3D data and does not require radiometric calibration.
Keywords: LIDAR; point cloud; laser scanning; ILRIS3D; lidar intensity
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MDPI and ACS Style
Wang, C.-K.; Lu, Y.-Y. Potential of ILRIS3D Intensity Data for Planar Surfaces Segmentation. Sensors 2009, 9, 5770-5782.
Wang C-K, Lu Y-Y. Potential of ILRIS3D Intensity Data for Planar Surfaces Segmentation. Sensors. 2009; 9(7):5770-5782.
Wang, Chi-Kuei; Lu, Yao-Yu. 2009. "Potential of ILRIS3D Intensity Data for Planar Surfaces Segmentation." Sensors 9, no. 7: 5770-5782.