Mobile robot systems performing simultaneous localization and mapping(SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian measurement noise,a robust SLAM algori...Mobile robot systems performing simultaneous localization and mapping(SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian measurement noise,a robust SLAM algorithm is proposed.It is based on the square-root cubature Kalman filter equipped with a Huber' s generalized maximum likelihood estimator(GM-estimator).In particular,the square-root cubature rule is applied to propagate the robot state vector and covariance matrix in the time update,the measurement update and the new landmark initialization stages of the SLAM.Moreover,gain weight matrices with respect to the measurement residuals are calculated by utilizing Huber' s technique in the measurement update step.The measurement outliers are suppressed by lower Kalman gains as merging into the system.The proposed algorithm can achieve better performance under the condition of non-Gaussian measurement noise in comparison with benchmark algorithms.The simulation results demonstrate the advantages of the proposed SLAM algorithm.展开更多
超分辨率图像复原是当今一个重要的热门研究课题.鉴于双边滤波优良的噪声抑制性和鲁棒的边缘保持性,提出一种双边滤波导出的广义MRF(Markov random field)图像先验模型.广义MRF模型不仅继承了双边滤波在阶数大邻域中的双重异性加权机制...超分辨率图像复原是当今一个重要的热门研究课题.鉴于双边滤波优良的噪声抑制性和鲁棒的边缘保持性,提出一种双边滤波导出的广义MRF(Markov random field)图像先验模型.广义MRF模型不仅继承了双边滤波在阶数大邻域中的双重异性加权机制,且简洁地建立了双边滤波与Bayesian MAP(maximum a posterior)方法之间的理论联系.同时,由广义MRF模型导出了一种各向异性扩散PDE(partial differential equation)的改进数值解法.随后,在MRF-MAP框架下分别考虑高斯噪声和脉冲噪声两种情形,提出一种基于广义Huber-MRF模型的超分辨率复原算法,理论上保证具有严格全局最优解,并且利用半二次正则化思想和最速下降法求解相应的最小能量泛函.不论是视觉效果方面,还是峰值信噪比(PSNR)方面,实验结果都验证了广义Huber-MRF模型在超分辨图像复原中具有更强的噪声抑制性和边缘保持能力.展开更多
基金Supported by the National High Technology Research and Development Program of China(2010AA09Z104)the Fundamental Research Funds of the Zhejiang University(2014FZA5020)
文摘Mobile robot systems performing simultaneous localization and mapping(SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian measurement noise,a robust SLAM algorithm is proposed.It is based on the square-root cubature Kalman filter equipped with a Huber' s generalized maximum likelihood estimator(GM-estimator).In particular,the square-root cubature rule is applied to propagate the robot state vector and covariance matrix in the time update,the measurement update and the new landmark initialization stages of the SLAM.Moreover,gain weight matrices with respect to the measurement residuals are calculated by utilizing Huber' s technique in the measurement update step.The measurement outliers are suppressed by lower Kalman gains as merging into the system.The proposed algorithm can achieve better performance under the condition of non-Gaussian measurement noise in comparison with benchmark algorithms.The simulation results demonstrate the advantages of the proposed SLAM algorithm.
基金Supported by the Key Science-Technology Project of Trigonal Yangtse River of China under Grant No.BE2004400 (长三角联合攻关重 大科技项目)the National Natural Science Foundation of China under Grant No.60672074 (国家自然科学基金)+3 种基金the National High-Tech Research and Development Plan of China under Grant No.2007AA12E100 (国家高技术研究发展计划(863))the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.M200606018 (国家教育部博士点基金)the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2006569 (江苏省自然科学基金)the Science- Technology Creation Plan for Graduate Students of Jiangsu Province of China (江苏省高校研究生科技创新计划)
文摘超分辨率图像复原是当今一个重要的热门研究课题.鉴于双边滤波优良的噪声抑制性和鲁棒的边缘保持性,提出一种双边滤波导出的广义MRF(Markov random field)图像先验模型.广义MRF模型不仅继承了双边滤波在阶数大邻域中的双重异性加权机制,且简洁地建立了双边滤波与Bayesian MAP(maximum a posterior)方法之间的理论联系.同时,由广义MRF模型导出了一种各向异性扩散PDE(partial differential equation)的改进数值解法.随后,在MRF-MAP框架下分别考虑高斯噪声和脉冲噪声两种情形,提出一种基于广义Huber-MRF模型的超分辨率复原算法,理论上保证具有严格全局最优解,并且利用半二次正则化思想和最速下降法求解相应的最小能量泛函.不论是视觉效果方面,还是峰值信噪比(PSNR)方面,实验结果都验证了广义Huber-MRF模型在超分辨图像复原中具有更强的噪声抑制性和边缘保持能力.
