This paper proposes parametric component and nonparametric component estimators in a semiparametric regression models based on least squares and weight function's method, their strong consistency and rib mean cons...This paper proposes parametric component and nonparametric component estimators in a semiparametric regression models based on least squares and weight function's method, their strong consistency and rib mean consistency are obtained under a locally generallied Gaussinan error's structure. Finally, the author showes that the usual weight functions based on nearest neighbor method satisfy the deigned assumptions imposed.展开更多
回环检测是消除同时定位与地图构建(simultaneous localization and mapping,SLAM)系统中累计误差的关键所在,在光照条件或视角变化较大的情况下,传统的基于外观的回环检测方法往往失效。针对这种情况,在ORBSLAM2的框架基础上提出一种...回环检测是消除同时定位与地图构建(simultaneous localization and mapping,SLAM)系统中累计误差的关键所在,在光照条件或视角变化较大的情况下,传统的基于外观的回环检测方法往往失效。针对这种情况,在ORBSLAM2的框架基础上提出一种物体级的回环检测方法。利用目标检测获得的语义信息和特征点信息构建物体级语义地图。将语义地图抽象成拓扑图并将地标抽象成节点,用颜色直方图描述节点信息,结合节点间的几何关系,基于语义和几何一致性约束,提出一种图匹配方法实现回环检测。当检测到回环时,通过物体对齐的方式进行回环校正。在公开的TUM和USTC数据集上进行实验,结果表明提出的系统精度较ORBSLAM2平均提高了49.58%,并且构建的语义地图显示出良好的定位效果。展开更多
目标定位是指利用传感信息估计目标在特定坐标系中的空间位置。基于到达时间(Time of Arrival,TOA)或等价的,基于距离的定位方法凭借其高精度特点得到了广泛研究。现有TOA定位方法一般假设传感器位置是精确的,只考虑距离测量噪声。而考...目标定位是指利用传感信息估计目标在特定坐标系中的空间位置。基于到达时间(Time of Arrival,TOA)或等价的,基于距离的定位方法凭借其高精度特点得到了广泛研究。现有TOA定位方法一般假设传感器位置是精确的,只考虑距离测量噪声。而考虑了传感器位置不确定性的文献通常缺少统计学优化与分析,无法得到一致性估计。本文同时考虑距离测量噪声和传感器部署不确定性,将目标位置与传感器坐标均当成未知变量构建最大似然问题。本文首先给出关于观测噪声和传感器空间分布的假设,以保证一致性估计器的存在性。有趣的是,本文分析了最大似然估计性质,证明了其不一定具有一致性。本文进一步变换原始观测方程,构建可最优求解的优化问题。特别地,针对距离测量噪声方差已知情况,构建了含二次目标函数和一个二次等式约束的广义信赖域问题,并给出了其最优解求解算法;针对距离测量噪声方差未知情况,构建了普通线性最小二乘问题,实现目标位置和距离测量噪声方差的同时估计。本文针对两种情况分别提出了相应的偏差消除方法,实现了一致估计,即随着观测数量增加,估计值收敛至真实目标位置。一致性特性使所提算法在大样本观测场景可实现超高精度定位。此外,推导了高斯-牛顿迭代算法,可在观测样本和传感器位置不确定性较小时提高算法定位精度。仿真结果验证了所得理论结果的正确性和所提算法在大样本观测下的优越性。展开更多
How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family ...How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.展开更多
With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interf...With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.展开更多
This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate s...This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.展开更多
文摘This paper proposes parametric component and nonparametric component estimators in a semiparametric regression models based on least squares and weight function's method, their strong consistency and rib mean consistency are obtained under a locally generallied Gaussinan error's structure. Finally, the author showes that the usual weight functions based on nearest neighbor method satisfy the deigned assumptions imposed.
文摘回环检测是消除同时定位与地图构建(simultaneous localization and mapping,SLAM)系统中累计误差的关键所在,在光照条件或视角变化较大的情况下,传统的基于外观的回环检测方法往往失效。针对这种情况,在ORBSLAM2的框架基础上提出一种物体级的回环检测方法。利用目标检测获得的语义信息和特征点信息构建物体级语义地图。将语义地图抽象成拓扑图并将地标抽象成节点,用颜色直方图描述节点信息,结合节点间的几何关系,基于语义和几何一致性约束,提出一种图匹配方法实现回环检测。当检测到回环时,通过物体对齐的方式进行回环校正。在公开的TUM和USTC数据集上进行实验,结果表明提出的系统精度较ORBSLAM2平均提高了49.58%,并且构建的语义地图显示出良好的定位效果。
文摘目标定位是指利用传感信息估计目标在特定坐标系中的空间位置。基于到达时间(Time of Arrival,TOA)或等价的,基于距离的定位方法凭借其高精度特点得到了广泛研究。现有TOA定位方法一般假设传感器位置是精确的,只考虑距离测量噪声。而考虑了传感器位置不确定性的文献通常缺少统计学优化与分析,无法得到一致性估计。本文同时考虑距离测量噪声和传感器部署不确定性,将目标位置与传感器坐标均当成未知变量构建最大似然问题。本文首先给出关于观测噪声和传感器空间分布的假设,以保证一致性估计器的存在性。有趣的是,本文分析了最大似然估计性质,证明了其不一定具有一致性。本文进一步变换原始观测方程,构建可最优求解的优化问题。特别地,针对距离测量噪声方差已知情况,构建了含二次目标函数和一个二次等式约束的广义信赖域问题,并给出了其最优解求解算法;针对距离测量噪声方差未知情况,构建了普通线性最小二乘问题,实现目标位置和距离测量噪声方差的同时估计。本文针对两种情况分别提出了相应的偏差消除方法,实现了一致估计,即随着观测数量增加,估计值收敛至真实目标位置。一致性特性使所提算法在大样本观测场景可实现超高精度定位。此外,推导了高斯-牛顿迭代算法,可在观测样本和传感器位置不确定性较小时提高算法定位精度。仿真结果验证了所得理论结果的正确性和所提算法在大样本观测下的优越性。
基金supported by National High Technology Research and Development Program of China (863 Program)(No.2007AA01Z416)National Natural Science Foundation of China (No.60773056)+1 种基金Beijing New Star Project on Science and Technology (No.2007B071)Natural Science Foundation of Liaoning Province of China (No.20052184)
文摘How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.
基金National Natural Science Foundation of China(No.62101219)Natural Science Foundation of Jiangsu Province(Nos.BK20201026,BK20210921)+1 种基金Science Foundation of Jiangsu Normal University(No.19XSRX006)Open Research Fund of Jiangsu Key Laboratory of Resources and Environmental Information Engineering(No.JS202107)。
文摘With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.
文摘This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.
