Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred ...Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.展开更多
位姿图优化(pose graph optimization,PGO)是3D SLAM(simultaneous localization and mapping)后端优化方法之一,其精确求解依赖于良好的初始值。针对PGO噪声数据集初始化,首先提出一种新的PGO目标公式——CN(chordal with noise)模型,...位姿图优化(pose graph optimization,PGO)是3D SLAM(simultaneous localization and mapping)后端优化方法之一,其精确求解依赖于良好的初始值。针对PGO噪声数据集初始化,首先提出一种新的PGO目标公式——CN(chordal with noise)模型,此模型考虑噪声影响下产生的旋转偏差,将偏差矩阵设为参数;其次,提出ORDM(optimize rotation with the deviation matrix)算法求解CN模型,此算法在位姿图子图中,分别建立关于偏差矩阵的相对旋转测量方程,最终将CN模型化为矩阵形式,并采用线性最小二乘求出偏差矩阵的封闭解,以此修正旋转方向。实验证明,ORDM算法在面对PGO噪声数据集时,较为鲁棒,具有一定的可伸缩性;与迭代初始化算法相比,可对应较差的初始化场景。展开更多
A retrospective analysis of the prognosis of Graves disease was carried on 224 cases, whose treatment has been stopped for 6 months to more than 10 years. The patients were divided into 3 groups. Group 1. 92 cases, re...A retrospective analysis of the prognosis of Graves disease was carried on 224 cases, whose treatment has been stopped for 6 months to more than 10 years. The patients were divided into 3 groups. Group 1. 92 cases, received long term antithyroid drug therapy: group 2. 100 cases, were展开更多
基金supported in part by the National Nature Science Foundation of China(62273239,62103283).
文摘Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm.
文摘位姿图优化(pose graph optimization,PGO)是3D SLAM(simultaneous localization and mapping)后端优化方法之一,其精确求解依赖于良好的初始值。针对PGO噪声数据集初始化,首先提出一种新的PGO目标公式——CN(chordal with noise)模型,此模型考虑噪声影响下产生的旋转偏差,将偏差矩阵设为参数;其次,提出ORDM(optimize rotation with the deviation matrix)算法求解CN模型,此算法在位姿图子图中,分别建立关于偏差矩阵的相对旋转测量方程,最终将CN模型化为矩阵形式,并采用线性最小二乘求出偏差矩阵的封闭解,以此修正旋转方向。实验证明,ORDM算法在面对PGO噪声数据集时,较为鲁棒,具有一定的可伸缩性;与迭代初始化算法相比,可对应较差的初始化场景。
文摘A retrospective analysis of the prognosis of Graves disease was carried on 224 cases, whose treatment has been stopped for 6 months to more than 10 years. The patients were divided into 3 groups. Group 1. 92 cases, received long term antithyroid drug therapy: group 2. 100 cases, were
