Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods

Abstract Terrestrial laser scanning is becoming a common surveying technique to measure quickly and accurately dense point clouds in 3-D. It simplifies measurement tasks on site. However, the massive volume of 3-D point measurements presents a challenge not only because of acquisition time and management of huge volumes of data, but also because of processing limitations on PCs. Raw laser scanner point clouds require a great deal of processing before final products can be derived. Thus, segmentation becomes an essential step whenever grouping of points with common attributes is required, and it is necessary for applications requiring the labelling of point clouds, surface extraction and classification into homogeneous areas. Segmentation algorithms can be classified as surface growing algorithms or clustering algorithms. This paper presents an unsupervised robust clustering approach based on fuzzy methods. Fuzzy parameters are analysed to adapt the unsupervised clustering methods to segmentation of laser scanner data. Both the Fuzzy C-Means (FCM) algorithm and the Possibilistic C-Means (PCM) mode-seeking algorithm are reviewed and used in combination with a similarity-driven cluster merging method. They constitute the kernel of the unsupervised fuzzy clustering method presented herein. It is applied to three point clouds acquired with different terrestrial laser scanners and scenarios: the first is an artificial (synthetic) data set that simulates a structure with different planar blocks; the second a composition of three metric ceramic gauge blocks (Grade 0, flatness tolerance ± 0.1 μm) recorded with a Konica Minolta Vivid 9i optical triangulation digitizer; the last is an outdoor data set that comes up to a modern architectural building collected from the centre of an open square. The amplitude-modulated-continuous-wave (AMCW) terrestrial laser scanner system, the Faro 880, was used for the acquisition of the latter data set. Experimental analyses of the results from the proposed unsupervised planar segmentation process are shown to be promising.