空地协同移动智能服务平台研发及典型应用

面向万物互联时代的移动智能服务需求,亟须建立空地协同智能服务平台。由于核心设备和算法的限制,空地协同移动智能服务平台发展较为缓慢。由此通过攻关传感器实时位置和姿态测量的核心技术与数据处理和分析的关键算法,研制了高精度位姿测量设备,研发了机载和车载空地多平台协同组网遥感观测系统,实现了多源遥感影像质量改善与智能精处理,有效支撑遥感测绘服务,提升遥感信息自动化处理水平,从而解决了空地协同智能服务平台中“准、快、灵”的难题,实现“北斗+位姿测量仪”全球自主“定基准”、空地协同组网快速“找目标”、智能化精处理自动“查变化”。成果已被广泛应用于地理国情监测、智慧城市规划和建设、应急测绘保障等行业。     

LiDAR室内检校模型粗差探测可靠性研究

基于机载LiDAR技术的测绘系统必需对其进行标定和校正,优化导航定位、定姿系统的测绘精度。冗余的观测数据不仅可以评估系统监测粗差的能力,还可以分析未检测粗差对导航结果的影响,评价结果的可靠程度。本文基于内、外可靠度理论,进行抗差检校模型的质量控制,实例分析表明该理论能有效衡量抗差模型对粗差的识别效果。     

低成本大视场深度相机阵列系统

在测距传感器不断轻量化、小型化以及室内外地图一体化导航应用的驱动下,三维（3D）室内移动测量成为当今研究和应用的热点,在室内建模、室内定位等新兴领域中的应用越来越广泛。3D室内移动测量系统通常配备激光扫描仪、全景相机、惯性测量单元（inertial measurement unit,IMU）系统和里程计等传感器,虽能实现3D室内点云数据的采集,但其距离传感器-激光扫描仪价格昂贵且便携性较差。彩色深度（RGB depth,RGB-D）相机为低成本3D室内移动测量系统构建提供了新的距离成像传感器选择,但主流型号RGB-D相机视场角小,继而导致数据采集效率远低于传统激光扫描仪,难以做到点云数据的完整覆盖与稳健采集,且易造成同时定位与制图（simultaneous localization and mapping,SLAM）过程中跟踪失败。针对以上问题,构建了一种低成本室内3D移动测量系统采集设备,通过组合多台消费级RGB-D相机构成大视场RGB-D相机阵列,提出了一种阵列RGB-D相机内外参数标定方法,并通过实验检验了设计系统采集的点云数据的精度。     

An automated seamless mosaicing system of multi-charge coupled devices of panchromatic data

Panchromatic data of pixel resolution 5.8 m obtained from IRS-1C and IRS-1D satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a combination of 3 CCD line sensors, each with 4096 pixels. Each CCD-line sensor with different imaging times causes geometric problems of mosaicing three strips data together. In this paper, we propose the details of the design elements of system that caters to the need for accurate and automatic multi strip image registration without any second resampling of the data. The systematic geometric correction grid mapping is improved to facilitate accurate mosaicing by automatic image registration task that makes use of the overlap data within image strips and image registration is achieved up to sub-pixel level.     

Developing Basic Design and Mathematical Framework for a Mobile Mapping System—a Case Study Using Available Sensors

There is a growing demand for technologies that support capturing of comprehensive and good quality 3D spatial data at a faster rate with low investment and minimal effort, while also causing least disturbance to other activities in the area. Mobile mapping systems (MMS), which are being developed in a few western countries, solve this problem but their import is highly expensive. While the components of a MMS are easily available off-the-shelf at lower cost, the main reason for their high cost is the intellectual property involved in the sensor design, integration, calibration, and the related software. Developing the intellectual property locally can bring down the cost of MMS to a large extent. Keeping this in mind, a MMS has been developed in this research using the locally available sensors. This paper describes the methodology to integrate navigation and mapping sensors including the developed calibration procedures. It further describes the time synchronization technique developed for multi-sensor data fusion and algorithms implemented by software package for data processing. The sensors integrated in the MMS include a standard GPS, IMU and a standard laser scanner. A Kalman filter is implemented to integrate the GPS and IMU data, which provides position and orientation information for the sensors. A simulation software package is also developed to verify, understand and develop the equations used in MMS. Field tests have been performed using the developed MMS and the results are shown for a few cases. Results validate the designed algorithms and indicate the successful development of the MMS, which has potential to be further developed and used in field. Though a number of papers are available on MMS, the thrust of this paper is to present a complete methodology for developing a MMS using locally available sensors. Unlike available papers, this paper outlines all aspects of design, calibration and operation, where each of these aspects is handled in a novel way as demanded by the available sensors. This is particularly useful for individuals or organizations interested in procuring sensor components off-the-shelf and develop their own (low cost) Mobile Mapping system.     

针对车载移动测量系统的交通标志自动检测

针对自然场景图像中交通标志的自动检测问题,提出了一种交通标志快速自动检测算法。该算法综合运用了自适应图像分割、灰度投影、形状分析和立体影像匹配等技术,实现了车载立体影像中道路交通标志的快速自动检测及其空间位置、尺寸等几何信息的计算。利用该算法对车载移动测量系统所拍摄的实际影像进行了实验,结果表明该算法具有鲁棒性强、检测速度快和检测准确率高等特点。     

车载POS系统测量合作目标安置参数解算方法

POS系统是移动测量系统的重要组成部分。由于系统集成影响,POS系统中心的运动状态无法直接观测。因此,可采取设置相关测量合作目标的方法,在确定其与POS系统中心的位置基础上,通过观测合作目标来确定POS系统中心的运动状态。从车载移动测量系统空间基准统一方程出发,提出了一种解算测量合作目标安置参数的方法,并以此为基础,系统分析POS系统定位定姿误差、全站仪测距测角误差、尺度因子误差等误差源对安置参数解算的影响,推导了安置参数解算的误差模型。实验结果表明,采用本文解算方法,可以获取毫米级的合作目标安置参数,满足合作目标应用于动态测量检测POS系统定位精度的需求。     

顾及平面特征的车载激光扫描系统外参数标定法

激光扫描仪外参数的标定是获取高精度三维地理信息数据的前提和保障。传统标定方法大多需布设特定的检校场、人工采集检校点,求解过程计算量较大。基于此,本文提出通过采集不同车行方向的同一区域的点云数据,提取平面特征数据并进行平面特征数据的自动化配准,对多个不同角度的平面特征的共同检校,实现三维空间中不同车行方向采集的相同地物点云的重合,最终完成系统外参数的标定的方法。试验结果显示,该方法对车载激光扫描系统外参数的标定实现了自动化,减少人工参与且达到了较高的检校精度。     

HERON LITE移动测量系统在房地一体中的应用

在开展房地一体项目时,传统的测量技术已不堪重负,即时定位与地图构建(SLAM)技术在移动测绘方面具有较好的应用,其不依托GNSS信号可以对室内和室外的地面水平环境进行地图构建和环境建模。文中利用HERON LITE便携式移动测量系统开展房地一体测量实验,介绍测图流程、数据内业处理与点云成图,并分析其点云精度,对比各种作业方式的效率。实验证明,该系统在保证精度的同时,可以提高房地一体测量效率。     

基于激光跟踪仪的传感器坐标标定应用研究

传感器空间位置标定是测量系统集成与安装的关键技术之一.根据激光跟踪仪的特点与优势,探讨了激光跟踪仪应用于移动测量多传感器坐标转换关系标定的可行性与适用性;重点探索了基于激光跟踪仪的传感器标定测量与数据处理方法,并分析了标定误差及其影响因素.针对船载多波束单体标定需求,基于Leica AT930型激光跟踪仪和经纬仪工业测...     

基于车载CCD影像的物方几何参数量算

介绍了用车载CCD相机所获得的影像测求物方空间目标几何参数的原则和方法，通过试验得出了一些有益的结论和建议。     

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.     

基于移动测量的交通标志自动提取

针对从移动测量车采集的图像中提取交通标志的需求,研究探索适合移动测量的交通标志检测识别方案。在交通标志检测阶段,提出一种检测多个圆形禁令标志连接的多圆度检测法,对于三角形警告标志的检测,在原有拐角检测法基础上提出基于候选区域的拐角检测法;在交通标志识别阶段,选用像素特征作为特征向量,采用概率神经网络实现交通标志的分类识别。最后设计实现移动测量后处理软件的交通标志自动提取模块。     

Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval

ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r²: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r²: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.     

车载移动测量系统研制与应用实践

为满足高精度空间信息获取的需求,本文在集成高端国产激光雷达、惯性测量单元等传感器的基础上,研发了新一代测绘级车载移动测量系统.设计优化了系统通信总线,对影响系统精度的因素进行了分析,并提出了激光雷达锥扫角标定方法及系统整体标定方法,提出了使用RTK优化POS数据的方法,精度达到厘米级.该系统在带状测图、高精度导航地图生...     

便携式消费级无人机相机标定有效性评估

本文针对近年来在各行业中广泛使用的便携式消费级无人机开展了相机标定有效性研究,分别通过室内标定场和后处理软件对相机进行标定和自标定,在对比无相机标定、飞前相机标定、飞后相机标定和自标定迭代校正等方法所得成图精度后,对不同相机标定方案的有效性进行了评估.结果表明:各种方案成图总体精度差别不大;飞前、飞后相机标定与无相机标...     

空地数据链系统中图像自动剪切方法研究

在空地数据链路通信中，鉴于存在需要传输的各种机载图像传感器的数据量越来越大与已有的数据链带宽相对较小的矛盾，为了提高数据链路在战场环境中的图像传输效率，对于用CCD航空相机以中心投影方式获取的静态图像数据，要求远程快速高保真地进行信息传递，提出了一种机载自动数据缩减方案。该方案的关键步骤是图像的自动匹配与剪切，它的实现算法利用了航拍相邻图像间具有强逻辑相关性的特点，基于二维仿射变换模型，使用通过傅里叶-梅林变换计算得到的图像匹配参数，在没有检测到突现目标的情况下，对相邻图像中的场景重叠部分进行自动剪切操作。自动剪切及镶嵌的试验结果表明，本算法复杂度较低，鲁棒性和可操作性较强，能够有效地缩减下行链路中待传输的数据量，具有进一步实用化的潜力。     

3D change detection at street level using mobile laser scanning point clouds and terrestrial images

Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes, robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes.     

基于IGS站组网的移动激光雷达测量系统整体检校

针对车载移动测量系统中激光扫描仪和载体坐标系之间存在的位置和姿态偏差,在结合常规特征点、特征面检校方法基础上,本文提出了一种带有误差改正数的位置和姿态检校方法。利用TLS获取的车载系统整体点云模型和传感器固有几何属性,获取传感器之间相对关系初值,在此基础上引入误差改正数,构建误差改正模型。在与IGS站联测的检校场中借助平面、球形标靶和平面反射标志等特征,采用最小二乘法迭代法计算误差改正数最优解,从而实现传感器快速检校。试验结果表明,该方法切实可行,检校后点云平面绝对精度和高程绝对精度分别为0.043、0.072 m,相对精度为0.018 m,满足移动测量系统数据获取的精度要求,对促进车载移动测量技术发展和应用具有重要意义。     

Development and Application of the Highway Mapping System of Ohio State University

Since 1990, the Center for Mapping at Ohio State University has been developing mobile mapping systems to automate and accelerate the collection of digital map data. These systems integrate the global positioning system (GPS) with inertial navigation units and imaging sensors to produce digital geographic data. They are modular and can be installed on various platforms, such as vans, trains and aeroplanes. New developments with the imaging system and the fully integrated data-processing environment are discussed in this article. The stereovision system enables the user to obtain three dimensional co-ordinates of any object in the field of view of the camera. Digital and video images, together with GPS positions, are stored in an object-oriented database, which merges data collected by airborne and terrestrial mapping platforms. Exploiting the enormous amount of information contained in the digital stereoscopic imagery is a challenging topic of current and future research.