基于点云的空间非合作目标位姿估计,常受到噪声影响.提出截断最小二乘估计与半定松弛(truncated least squares estimation and semidefinite relaxation,TEASER)与迭代最近点(iterative closest point,ICP)的结合算法,提升空间非合作...基于点云的空间非合作目标位姿估计,常受到噪声影响.提出截断最小二乘估计与半定松弛(truncated least squares estimation and semidefinite relaxation,TEASER)与迭代最近点(iterative closest point,ICP)的结合算法,提升空间非合作目标位姿估计精度与鲁棒性.该方法包括粗配准与精配准两个环节:在粗配准环节中,基于局部点云与模型点云的方向直方图特征(signature of histogram of orientation,SHOT)确定匹配对,利用TEASER算法求解初始位姿;在精配准环节中,可结合ICP算法优化位姿估计结果.北斗卫星仿真实验表明:在连续帧位姿估计中,噪声标准差为3倍点云分辨率时,基于TEASER的周期关键帧配准方法的平移误差小于3.33 cm,旋转误差小于2.18°;与传统ICP方法相比,平均平移误差与平均旋转误差均有所降低.这表明所提出的空间非合作目标位姿估计方法具有良好的精度和鲁棒性.展开更多
This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimat...This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimates the coordinates of senor nodes using the Free Search optimization.Compared to the least-squares estimation algorithms,the localization accuracy has been increased significantly,which has been verified by the simulation results.展开更多
This paper addresses the learning algorithm on the unit sphere.The main purpose is to present an error analysis for regression generated by regularized least square algorithms with spherical harmonics kernel.The exces...This paper addresses the learning algorithm on the unit sphere.The main purpose is to present an error analysis for regression generated by regularized least square algorithms with spherical harmonics kernel.The excess error can be estimated by the sum of sample errors and regularization errors.Our study shows that by introducing a suitable spherical harmonics kernel,the regularization parameter can decrease arbitrarily fast with the sample size.展开更多
文摘基于点云的空间非合作目标位姿估计,常受到噪声影响.提出截断最小二乘估计与半定松弛(truncated least squares estimation and semidefinite relaxation,TEASER)与迭代最近点(iterative closest point,ICP)的结合算法,提升空间非合作目标位姿估计精度与鲁棒性.该方法包括粗配准与精配准两个环节:在粗配准环节中,基于局部点云与模型点云的方向直方图特征(signature of histogram of orientation,SHOT)确定匹配对,利用TEASER算法求解初始位姿;在精配准环节中,可结合ICP算法优化位姿估计结果.北斗卫星仿真实验表明:在连续帧位姿估计中,噪声标准差为3倍点云分辨率时,基于TEASER的周期关键帧配准方法的平移误差小于3.33 cm,旋转误差小于2.18°;与传统ICP方法相比,平均平移误差与平均旋转误差均有所降低.这表明所提出的空间非合作目标位姿估计方法具有良好的精度和鲁棒性.
基金National Research Foundation for the Doctoral Program of Higher Education of China(No.20060266006)the High-school Natural Science Research Foundation of Jiangsu Province(No.07KJB510095)
文摘This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimates the coordinates of senor nodes using the Free Search optimization.Compared to the least-squares estimation algorithms,the localization accuracy has been increased significantly,which has been verified by the simulation results.
基金supported by National Natural Science Foundation of China (Grant Nos. 61272023 and 61075054)
文摘This paper addresses the learning algorithm on the unit sphere.The main purpose is to present an error analysis for regression generated by regularized least square algorithms with spherical harmonics kernel.The excess error can be estimated by the sum of sample errors and regularization errors.Our study shows that by introducing a suitable spherical harmonics kernel,the regularization parameter can decrease arbitrarily fast with the sample size.
