视觉里程计研究综述

视觉里程计（Visual Odometry）作为视觉同步定位与地图构建技术（Visual Simultaneous Localization and Mapping）的一部分,主要通过相机传感器获取一系列拥有时间序列图像的信息,从而预估机器人的姿态信息,建立局部地图,也被称为前端,已经被广泛应用在了多个领域,并取得了丰硕的实际成果,它对于无人驾驶、全自主无人机、虚拟现实和增强现实等方面有着重要意义.本文在介绍经典视觉里程计技术框架模块中的各类算法的基础上,对近年来新颖的视觉里程计技术（VO）的研究和论文进行了总结,按照技术手段不同分为两大类——多传感器融合的视觉里程计（以惯性视觉融合为例）和基于深度学习的视觉里程计.前者通过各传感器之间的优势互补提高VO的精度,后者则是通过和深度学习网络结合改善VO的性能.最后通过比较视觉里程计现有算法,并结合VO面临的挑战展望了视觉里程计的未来发展趋势.     

基于多传感器观测下的带乘性噪声系统的最优滤波融合算法

多传感器系统由于具有较高的精度及可靠性日益受到研究者的重视。本文针对多传感器观测下的带乘性噪声系统 ,分别给出了集中式与分布式滤波融合算法。这 2种算法在数学上完全等价 ,在线性最小方差意义上均是最优的。但对于工程实际问题 ,分布式的融合策略在计算、可靠性、故障的检测与隔离等方面更具有优势。文中举例验证了这 2种融合算法的有效性。     

微PNT与综合PNT

综合定位导航授时是后GNSS(全球卫星导航系统)发展的必然趋势。本文侧重梳理微PNT发展需求和发展现状,分析与之关联的核心关键技术,论述综合PNT与微PNT的关系。强调综合PNT需要大量基础设施投入与建设,而微PNT侧重高技术传感器的集成应用。与现有文献思路不同的是,我们认为,为了实现各类传感器PNT输出结果的坐标基准统一和时间尺度一致,微PNT应包含多模GNSS和芯片级惯性导航和芯片级原子钟等定位定时组件,微PNT强调小型化、个性化、组合化服务终端;微PNT除各PNT组件的小型化外,还包括各组件的深度集成,各类数据的自适应融合和各组件的自主标校;当然,微PNT也强调各传感器信息的时空基准的统一。     

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.     

Identifying tropical dry forests extent and succession via the use of machine learning techniques

Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.     

天地图省、市、县分级节点电子地图数据融合的分析与实现——以浙江省嘉兴市为例

基于"天地图·嘉兴"省市级与市县级电子地图分级节点数据融合实施的必要性与实现方法,介绍了分级节点电子地图数据融合的技术路线和技术流程,可为天地图其他城市级节点电子地图数据融合工作提供参考。     

机载LiDAR数据和航空影像基于角特征的配准

机载激光雷达(Light Detection And Ranging,LiDAR)系统近年来逐渐成为一种新兴的测量手段,是一种可以直接提供大量地面离散点的高精度三维信息的主动式遥感技术,具有依赖控制测量条件少、受天气影响小、自动化水平高等优点。然而LiDAR获取的数据主要是离散激光点云,具有高精度的空间结构信息而缺少纹理和光谱信息。将LiDAR数据和遥感影像数据进行融合将是当今测绘科学研究的方向之一。融合影像数据和点云数据的关键步骤就是将两者进行高精度的配准。     

基于位姿传感器的户外ARGIS注册技术

针对传统基于计算机视觉注册方法的局限性,提出并实践将GPS与位姿传感器进行组合应用于户外环境下增强现实地理信息系统的注册过程。设计了一套在户外注册单元硬件设备系统,推导阐释了实虚融合坐标转换关系,解决了不同设备间涉及的硬件配准集成问题。实验结果表明,该系统的实时性与准确性指标能够达到校园环境下户外注册的技术要求。     

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Abstract

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods.     

基于改进的IHS方法的SAR图像增强与融合

从目前SAR图像处理及其应用的研究现状来看,SAR图像和可见光图像相结合的处理和应用模式已得到公认,但是这种模式并不是完美的。探究了在没有可见光数据情况下SAR图像的增强和融合的方法,提出了一种基于改进的IHS方法的SAR图像增强与融合的算法。实践证明,这种改进的IHS方法融合的图像比传统的IHS方法融合的图像具有更高的对比度和清晰度。