Robots have the demand to interact with different objects within their expected visual range,thus precise pose estimation of target objects is in urgent needs.In this article,a novel pose estimation method is proposed based on viewpoint feature histogram and ICP algorithm using 3D point c loud data gathered by laser range finder (LRF).Firstly,point clo ud data is collected around the target object,then we register different point clouds together and get the comp lete point cloud model for the target object.Next,we calculate viewpoint feature histogram for every point cloud mode l and build a database filled with feature histograms from different views.Once we get a point cloud from a new po sition,we can search the most appropriate candidate in the database using KNN algorithm,and use it as initial value of posture matrix.Finally, ICP algorithm is utilized to minimize registration error and get the precisely e stimated posture matrix,and the whole process stops when the result is precise enough or the number of iterations exce eds the limit.We test this method with both simulations and experiments,and the results that this method is of strong robustness when estimating posture of the target objects.