Geometrical Feature Extraction Using 2D Range Scanner
Sen Zhang, Martin David Adams, Fan Tang, Lihua Xie · 2003
In this paper, two new algorithms for feature detection are presented. The first one can detect edge and circle features accurately using the Gaussian-Newton optimization method to fit the circle parameters. It consists of two parts: the first is the segmentation of data of each scan which is followed by parameter acquisition. The algorithm is off-line in nature as the segmentation and parameter acquisition are carried out after each scan data is collected. We also present another algorithm which is on-line and applies a multiple models filtering approach to handle features of different geometries such as lines and circles. It detects the circle features using the unscented Kalman filter. Experimental results show that the proposed two approaches are efficient in detecting features.