A WOA-CSF adaptive filtering method for complex terrain scene point clouds
In order to solve the problem of poor adaptivity of the cloth simulation filtering(CSF)algorithm,an improved CSF algorithm(WOA-CSF)based on the whale optimization algorithm(WO A)and adaptive parameter tuning is pro-posed.In this paper,a fitness evaluation function based on the minimum error rate of misclassified point clouds as the criterion is constructed,then the WO A algorithm is used to adaptively optimize the four parameters of the CSF algo-rithm,and the WOA-CSF filtering algorithm is constructed,and finally the comparative study of the filtering experi-ments of the WOA-CSF algorithm and the CSF algorithm is carried out.The experimental results show that the average Kappa coefficient of WOA-CSF algorithm in four complex environments such as cities,towns,villages and mountainous areas improves from 68.33％to 81.54％,the average total error rate decreases from 10.54％to 6.62％,and the average class Ⅰ error rate decreases from 25.87％to 6.77％.In the complex scene,the non-ground points are well filtered while the terrain features are retained to a great extent.
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