RGB-D SLAM综述

RGB-D SLAM是指使用RGB-D相机作为视觉传感器,进行同时定位与地图构建(SLAM)的技术。RGB-D相机是近几年推出的能够同时采集环境RGB图像和深度图像的视觉传感器。首先对主流RGB-D相机,RGB-D SLAM算法框架流程做了介绍,然后对RGB-D SLAM算法的国内外主要标志性成果,以及RGB-D SLAM的研究现状进行介绍,并对RGB-D SLAM方法前端视觉里程计中特征检测与匹配、后端位姿图优化、回环检测等关键技术进行介绍总结。最后,对RGB-D SLAM算法的优缺点进行了分析,并对RGB-D SLAM算法的研究热点及发展趋势进行了讨论。     

基于黎曼距离的编队飞行卫星姿态控制系统故障检测数据优选方法

为突破星上资源的严苛约束、提升编队飞行卫星故障检测能力,提出了一种适用于编队飞行卫星姿态控制系统的故障检测数据优选方法,通过利用黎曼距离衡量无故障情况和故障情况下系统测量信息之间的差距,构建了编队飞行卫星姿态控制系统故障检测能力的评价指标。在此基础上,提出了故障检测数据优选方法,为系统输出信息结构优化提供了理论依据。仿真表明,所提出的方法能够在星上资源受限的情况下有效提升编队飞行卫星姿态控制系统的故障检测能力。     

高速BPSK相干光通信系统对激光器线宽要求

文章概述了激光器相位噪声对零差相干检测光通信系统性能的影响，对特定的基于平衡探测的科斯塔斯（Costas）环路接收系统相位误差进行了详细的理论分析，最后获得了不同传输码速率下的零差相干检测光通信系统对激光器线宽的具体要求。     

Constellation analysis of an integrated AIS/remote sensing spaceborne system for ship detection

A future system integrating data from remote sensing and upcoming AIS satellites is analyzed through the development of a novel design method for global, discontinuous coverage constellations. It is shown that 8 AIS satellites suffice to guarantee global coverage and a ship location update of 50 min if the spaceborne AIS receiver has a swath of 2800 nm. Furthermore, synergic utilization of COSMO/SkyMed and Radarsat-C data would provide a mean revisit time of 7 h, with AIS information available within 25 min from SAR data acquisition.     

ASW-RTS:一种软件性能数据在线检测方法

在软件健康管理系统中,需要对相关被测构件的性能进行在线检测,从而确定构件的健康状态。针对被测构件的性能衰退检测问题,在AR(p)模型基础上,结合时间序列滚动式算法的准确性,提出了一种具有自适性策略的ASWRTS算法,用于对被测构件革新异常类性能数据的在线检测,提高检测的准确度,并基于该算法设计了构件性能异常数据的在线检测过程。实验结果表明,该算法将检测精度提高了50%左右,能够有效在线检测构件的性能衰退,说明了该算法的正确性和有效性。     

波音737系列飞机过热探测系统原理分析及排故技巧

轮舱火警、机翼及后下部机身的过热探测系统是飞机防火系统的重要组成部分，且故障发生频繁。本文介绍了波音737系列飞机的轮舱火警、机翼及后下部机身的过热探测系统的基本原理及常见故障，并结合外场维修经验介绍了排除该系统故障的一些技巧。     

Hypothesis-test-based landcover change detection using multi-temporal satellite images – A comparative study

Remote sensing images and technologies have been widely applied to environmental monitoring, in particular landuse/landcover classification and change detection. However, the uncertainties involved in such applications have not been fully addressed. In this paper two hypothesis-test-based change detection methods, namely the bivariate joint distribution method and the conditional distribution method, are proposed to tackle the uncertainties in change detection by making decisions based on the desired level of significance. Both methods require a data set of class-dependent no-change pixels to form the basis for class-dependent hypothesis test. Using an exemplar study area in central Taiwan, performance of the proposed methods are shown to be significantly superior to two other commonly applied methods (the post-classification comparison and the image differencing methods) in terms of the overall change detection accuracies. The conditional distribution method takes into consideration the correlation between digital numbers of the pre- and post-images and the effect of the known pre-image digital number on the range of the post-image digital number, and therefore yields the highest change detection accuracy. It is also demonstrated that the class-dependent change detection is crucial for accurate landuse/landcover change detection.     

一种遥感图像建筑物检测新方法

遥感图像中的建筑物检测对于土地规划和地图绘制等具有重要意义。文章针对高分辨率全色遥感图像植被覆盖中隐蔽建筑物检测问题,提出了一种结合屋顶结构信息和纹理信息的快速房屋检测方法。首先利用屋顶边缘的几何关系寻找具有矩形屋顶的建筑物;然后使用形态学方法提取屋顶较亮的建筑物;最后利用局部二值模式（Local Binary Pattern,LBP）描述屋顶的纹理特征,并利用支持向量机（Support Vector Machine,SVM）排除误检区域。通过在全色遥感图像集上的试验证明,所提方法对高分辨率遥感图像植被覆盖区域中的建筑物目标具有较高检测率和较低误检率。     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.     

基于模块度的复杂网络社团结构聚类新算法

针对传统的分裂聚类算法删除连边会影响节点划分的问题,结合K-means算法的思想并加以改进,提出了一种以邻居节点为聚类备选集,所有社团中心节点同时聚类的同步聚类算法。该算法根据节点中心度和最短路变化率确定社团中心节点集,然后以中心节点为社团中心,以邻居节点为聚类备选集合进行聚类,完成社团的划分。将算法应用到Zachary网络中并与GN算法、Top Leader算法进行比较,仿真结果表明该算法对网络有较好的划分。