An Improved Genetic Clustering Algorithm for Feature Extraction of Laser Scanner
YU Jin-Xia1,2, CAI Zi-Xing2, DUAN Zhuo-Hua2,3
1.College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 4540032. College of Information Science and Engineering, Central South University, Changsha 4100833. Department of Computer Science, Shaoguan University, Shaoguan 512003
Abstract：To automatically extract the environmental feature obtained by 2D laser scanner, an improved genetic clustering algorithm is presented. Firstly, a weighted fuzzy clustering algorithm is introduced to realize feature extraction of laser scanner after integrating the spatial neighboring information of range data into fuzzy clustering algorithm. Then, aiming at the unknown clustering number, the validities of different clustering algorithms are estimated by choosing a suitable index function for the fitness function of genetic algorithm. Moreover, to solve the local optimum of clustering algorithm, the genetic clustering algorithm is improved. The population diversity is increased and the genetic operators of elitist rule are improved to enhance the local search capacity and speed up the convergence. Compared with other algorithms, the effectiveness of the proposed algorithms is demonstrated.
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2008, Vol. 21 
