中文摘要：

针对散乱点云中3维目标的位姿参数估计问题,提出一种基于超二次曲面模型的3维目标定位算法.该算法利用3维目标的超二次曲面部件化模型,定义了空间点与任意位姿下3维目标的归一化径向欧氏距离.根据点云中目标表面各点到目标的均方距离,以及目标表面点数和内部点数等信息,建立了3维目标位姿估计的非线性目标函数,从而将目标定位问题转化为该目标函数的优化问题.采用入侵性杂草优化（IWO）算法优化该目标函数,将获得的最优解作为3维目标的位姿参数估计值.实验结果表明,该算法的目标定位精度高,位姿参数估计的一致性好,且能有效抑制测量噪声对定位结果的影响.

英文摘要：

For the pose parameter estimation problem of 3D objects in an unorganized point cloud,a 3D object localization algorithm based on superquadrics model is proposed.A normalized radial Euclidean distance of a space point from the 3D object under arbitrary pose is defined by using the part-based superquadrics model of the 3D object.Then a nonlinear objective function for the 3D object pose estimation is established according to the mean square distance between the object surface points and the object in the point cloud,as well as the surface point number and interior point number of the object.By this means,the object localization problem is transformed into an optimization problem of the objective function.Then the invasive weed optimization（IWO） algorithm is adopted to optimize this objective function,and the obtained optimal solution is used as the estimation value of the 3D object pose.Experimental results demonstrate that the proposed algorithm can yield accurate object localization results with a good consistency of pose parameters,and effectively suppress the influence of measurement noises on measurement results.