Superquadric Modeling And Localization Of 3D Object Based On Point Cloud Data

Accurate recognition and localization of 3D object are the foundation for the intelligent robot to perform subsequent tasks.With the continuous expansion of the application domains of intelligent robots,the work scenes of robots are more and more complex.And the traditional 3D object modeling and localization method are hard to meet the requirements of accurate manipulation for intelligent robots in complex environment.And with the continuous development of range sensing technologies,the measurement accuracy and efficiency of 3D point cloud have been greatly improved,and it is increasingly used in robots.Therefore,study on 3D object modeling and localization based on point cloud data has important theoretical and practical value for improving the environment awareness perception of intelligent robots.In this thesis,the problems of 3D point cloud data acquisition,3D object modeling and localization are deeply studied.According to the distance and space position relationship between space point and superquadric surfaces,the 3D object modeling and localization problems are transformed into the optimization problem of objective function.And swarm intelligence optimization algorithms,such as invasive weed optimization(IWO)algorithm and the success-history based parameter adaptive differential evolution(SHADE)algorithm,are adopted to optimize the problem.The main study contents are as follows:(1)The principle of acquiring point cloud data by 3D laser scanner,how to convert depth image acquired by Kinect into 3D point cloud data,and filtering preprocessing of 3D point cloud are introduced.(2)Aiming at the problem of insufficient parameter fitting accuracy in the modeling process of 3D object superquadric,an optimization algorithm combining IWO algorithm and Levenberg-Marquardt(LM)algorithm is presented.The problem that the LM algorithm relies on the initial value in the optimization objective function is solved,thereby avoiding the optimization algorithm falling into the local minimum value and ensuring the accuracy of the optimization result.The experimental results show that the proposed algorithm has high accuracy in fitting parameters of superquadric model,and can effectively suppress the noise interference as well.(3)Aiming at the problem of 3D object localization in unordered point cloud,a 3D object localization algorithm based on superquadric is presented.According to the distance relationship between the point in the point cloud and the superquadric model,the point is determined to belong to a superquadric,and the nonlinear objective function of the 3D object pose estimation is established.Thereby,the localization problem of 3D object is transformed into the minimization problem of the objective function.The SHADE algorithm is adopted to minimize the objective function,and estimation of 3D object pose parameters is realized.The experimental results show that the proposed algorithm has high accuracy and good consistency of pose estimation,and can suppress the influence of measurement error on the localization result.