The uncertainty of measurements and map features in quantity,as well as Data Association(DA)between measurements and map features,are two prevalent challenges in Simultaneous Localization and Mapping(SLAM).By leveragi...The uncertainty of measurements and map features in quantity,as well as Data Association(DA)between measurements and map features,are two prevalent challenges in Simultaneous Localization and Mapping(SLAM).By leveraging Random Finite Set(RFS)theory,SLAM can be naturally formulated as a complete Bayesian estimation problem.In this article,we begin by performing a recursive Bayesian estimator to propagate the joint probability density of the platform's pose and map,and then derive the marginal probability densities for the pose and map individually.Thus,we propose a Pose and Map Alternating Update(PMAU)-SLAM,which achieves favorable linear computational complexity with respect to the number of landmarks in the Field-of-View(FOV).This approach maintains a single probabilistic representation of the map,avoiding the need for multiple parallel maps fusion,as typically required by a particle-based SLAM method.We consider the propagation of the Probability Hypothesis Density(PHD)for the map RFS and the pose probability density,leading to the derivation of the PHD-PMAU-SLAM method.The labeled and unlabeled Gaussian Mixture(GM)-PHD-PMAU-SLAM algorithms are introduced,in which GM models,the Unscented Kalman Filter(UKF)and Covariance Intersection(CI)are used to address PHD approximation,nonlinear filtering,and pose fusion,respectively.Experimental results on both simulated and real-world datasets demonstrate that the proposed methods improve the accuracy and robustness of landmark-based SLAM in cluttered environments while remaining computationally efficient.展开更多
In this paper,we study how to design filters for nonlinear uncertain systems over sensor networks.We intoduce two Kalmantype nonlinear fitrs in centralied and dstrbute frameworks.Moreover,the tuning method for the par...In this paper,we study how to design filters for nonlinear uncertain systems over sensor networks.We intoduce two Kalmantype nonlinear fitrs in centralied and dstrbute frameworks.Moreover,the tuning method for the parameters of the filteres is established to ensure the consistency,i.e..the mean square error is upper bounded by a known parameter matrix at each time.We apply the consistent fiters to the track to-track association analysis of multi targets with uncertain dynamics.A novel track to-track asocaion algoritm is proposed to idenify whether two tracks are from the same target.It is proven that the resulting probability of mis.asociation is lower than the desired threshold.Numerical simulations on track.to track association are given to show the ffetives of the methods.展开更多
基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全...基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全球浅水模式对接,检验了HBFNEn KF同化方法的有效性。同时,对比了集合均方根滤波(En SRF)、HNEn KF(Hybrid Nudging En KF)、HBFNEn KF三种方法在有误差模式中的同化效果。试验结果表明:HBFNEn KF同化方法保留了HNEn KF方法的同化连续性,解决了En KF同化不连续不平滑的问题,同时还有着更快的收敛速度;当采用单变量分析试验时,HBFNEn KF方法的优势最为明显,表明HBFNEn KF能够较好地保持不同模式变量间的平衡。此外,增量场尺度分析结果表明:相比En SRF,HBFNEn KF在大尺度范围有更好的同化效果,同时能够避免在中小尺度范围内出现大量的虚假增量。展开更多
目的使用儿童胸部模型评估滤波反投影(Filtered Back Projection,FBP)算法与AIDR 3D算法对噪声降低和优化图像质量的差异。方法一个仿真1岁儿童胸部模型的胸腔内配备了6组含有不同碘浓度的管状塑料管,浓度范围0.89~5.29 mgI/mL。该模型...目的使用儿童胸部模型评估滤波反投影(Filtered Back Projection,FBP)算法与AIDR 3D算法对噪声降低和优化图像质量的差异。方法一个仿真1岁儿童胸部模型的胸腔内配备了6组含有不同碘浓度的管状塑料管,浓度范围0.89~5.29 mgI/mL。该模型在320排CT扫描仪选用低管电压(80 kV)和低电流(13、16、19、22、24、27 mAs)下扫描。图像重建采用FBP、自适应迭代AIDR 3D和AIDR 3D Strong三种方法。测量不同碘浓度的塑料管内和模型组织部分的CT值、图像噪声、对比噪声比(Contrast to Noise Ratio,CNR)。两位放射科医生对影像质量进行独立评估。结果相对于FBP法,AIDR 3D在相同剂量水平下有效降低图像噪声。采用AIDR 3D Strong算法对80 kV和13、16、19、22 mAs儿童胸部模型图像进行了高质量评分(AIDR 3D Strong:3.85±0.39,AIDR 3D:3.54±0.46,FBP:3.17±0.68)。与相同的低剂量方案相比,使用AIDR 3D Strong重建试管(4.42 mgI/mL)的CNR值高于FBP重建试管(5.29 mgI/mL)。结论相比FBP,AIDR 3D Strong显著降低了图像噪声,提高图像质量。对于儿童CT扫描在低辐射剂量下,有进一步降低造影剂的浓度的可能。儿科胸部模型验证可尝试多种低浓度碘化造影剂和低剂量扫描,优化CT扫描方案。展开更多
基金partly supported by the Technology Foundation for Basic Enhancement Plan,China(No.2021-JCJQ-JJ-0301)the National Natural Science Foundation of China(Nos.U22A2044,U22A2047,and 62371173)the Hangzhou Leading Innovation and Entrepreneurship Team,China(No.STD013)。
文摘The uncertainty of measurements and map features in quantity,as well as Data Association(DA)between measurements and map features,are two prevalent challenges in Simultaneous Localization and Mapping(SLAM).By leveraging Random Finite Set(RFS)theory,SLAM can be naturally formulated as a complete Bayesian estimation problem.In this article,we begin by performing a recursive Bayesian estimator to propagate the joint probability density of the platform's pose and map,and then derive the marginal probability densities for the pose and map individually.Thus,we propose a Pose and Map Alternating Update(PMAU)-SLAM,which achieves favorable linear computational complexity with respect to the number of landmarks in the Field-of-View(FOV).This approach maintains a single probabilistic representation of the map,avoiding the need for multiple parallel maps fusion,as typically required by a particle-based SLAM method.We consider the propagation of the Probability Hypothesis Density(PHD)for the map RFS and the pose probability density,leading to the derivation of the PHD-PMAU-SLAM method.The labeled and unlabeled Gaussian Mixture(GM)-PHD-PMAU-SLAM algorithms are introduced,in which GM models,the Unscented Kalman Filter(UKF)and Covariance Intersection(CI)are used to address PHD approximation,nonlinear filtering,and pose fusion,respectively.Experimental results on both simulated and real-world datasets demonstrate that the proposed methods improve the accuracy and robustness of landmark-based SLAM in cluttered environments while remaining computationally efficient.
基金the National Natural Science Foundation of China(Nos.11931018,61973299)the Beijing Advanced Innovation Center for Intelligent Robots and Systems(No.2019IRS09).
文摘In this paper,we study how to design filters for nonlinear uncertain systems over sensor networks.We intoduce two Kalmantype nonlinear fitrs in centralied and dstrbute frameworks.Moreover,the tuning method for the parameters of the filteres is established to ensure the consistency,i.e..the mean square error is upper bounded by a known parameter matrix at each time.We apply the consistent fiters to the track to-track association analysis of multi targets with uncertain dynamics.A novel track to-track asocaion algoritm is proposed to idenify whether two tracks are from the same target.It is proven that the resulting probability of mis.asociation is lower than the desired threshold.Numerical simulations on track.to track association are given to show the ffetives of the methods.
文摘基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全球浅水模式对接,检验了HBFNEn KF同化方法的有效性。同时,对比了集合均方根滤波(En SRF)、HNEn KF(Hybrid Nudging En KF)、HBFNEn KF三种方法在有误差模式中的同化效果。试验结果表明:HBFNEn KF同化方法保留了HNEn KF方法的同化连续性,解决了En KF同化不连续不平滑的问题,同时还有着更快的收敛速度;当采用单变量分析试验时,HBFNEn KF方法的优势最为明显,表明HBFNEn KF能够较好地保持不同模式变量间的平衡。此外,增量场尺度分析结果表明:相比En SRF,HBFNEn KF在大尺度范围有更好的同化效果,同时能够避免在中小尺度范围内出现大量的虚假增量。
文摘目的使用儿童胸部模型评估滤波反投影(Filtered Back Projection,FBP)算法与AIDR 3D算法对噪声降低和优化图像质量的差异。方法一个仿真1岁儿童胸部模型的胸腔内配备了6组含有不同碘浓度的管状塑料管,浓度范围0.89~5.29 mgI/mL。该模型在320排CT扫描仪选用低管电压(80 kV)和低电流(13、16、19、22、24、27 mAs)下扫描。图像重建采用FBP、自适应迭代AIDR 3D和AIDR 3D Strong三种方法。测量不同碘浓度的塑料管内和模型组织部分的CT值、图像噪声、对比噪声比(Contrast to Noise Ratio,CNR)。两位放射科医生对影像质量进行独立评估。结果相对于FBP法,AIDR 3D在相同剂量水平下有效降低图像噪声。采用AIDR 3D Strong算法对80 kV和13、16、19、22 mAs儿童胸部模型图像进行了高质量评分(AIDR 3D Strong:3.85±0.39,AIDR 3D:3.54±0.46,FBP:3.17±0.68)。与相同的低剂量方案相比,使用AIDR 3D Strong重建试管(4.42 mgI/mL)的CNR值高于FBP重建试管(5.29 mgI/mL)。结论相比FBP,AIDR 3D Strong显著降低了图像噪声,提高图像质量。对于儿童CT扫描在低辐射剂量下,有进一步降低造影剂的浓度的可能。儿科胸部模型验证可尝试多种低浓度碘化造影剂和低剂量扫描,优化CT扫描方案。
