Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Real-time Visualization of Urban with Gigantic Amount of Detailed Buildings

Complex urban scenery is generally composed of gigantic amount of detailed buildings, efficient representation and rendering are essential for its visualization. We present an accelerating method for urban visualization. Our approach can optimize the organization of models in accordance with the quadtree based terrain, which makes the parallelization easier. Through minimizing the draw call within one rendering process, our approach can reduce the time cost of each frame and improve the framerate greatly. Hereby, our system can handle large-scale detailed models with high diversity and also can afford the ability to adjust the rendering strategy automatically according to the state of the hardware.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Map Building for a Mobile Robot Based on Grey System Theory

In this paper, a new method for mobile robot map building based on grey system theory is presented, by which interpretation and integration of sonar readings can be solved robustly and efficiently. The conception of 'grey number is introduced to model and handle the uncertainty of sonar reading. A new data fusion approach based on grey system theory is proposed to construct environment model. Map building experiments are performed both on a platform of simulation and a real mobile robot. Experimental results show that our method is robust and accurate.

Vision-based Recognition Algorithm for Up-To-Date Indoor Digital Map Generations at Damaged Buildings

When firefighters are engaged in search and rescue missions inside a building at a risk of collapse,they have difficulty in field command and rescue because they can only simplymonitor the situation inside the building utilizing old building drawings or robots.To propose an efficient solution for fast search and rescue work of firefighters,this study investigates the generation of up-to-date digital maps for disaster sites by tracking the collapse situation,and identifying the information of obstacles which are risk factors,using an artificial intelligence algorithm based on low-cost robots.Our research separates the floor by using the mask regional convolutional neural network(R-CNN)algorithm,and determines whether the passage is collapsed or not.Then,in the case of a passage that can be searched,the floor pattern of the obstacles that exist on the floor that has not collapsed is analyzed,and obstacles are searched utilizing an image processing algorithm.Here,we can detect various unknown as well as known obstacles.Furthermore,the locations of obstacles can be estimated using the pixel values up to the bounding box of an existing detected obstacle.We conduct experiments using the public datasets collected by Carnegie Mellon university(CMU)and data collected by manipulating a low-cost robot equipped with a smartphone while roaming five buildings in a campus.The collected data have various floor patterns for objectivity and obstacles that are different from one another.Based on these data,the algorithm for detecting unknown obstacles of a verified study and estimating their sizes had an accuracy of 93%,and the algorithm for estimating the distance to obstacles had an error rate of 0.133.Through this process,we tracked collapsed passages and composed up-to-date digital maps for disaster sites that include the information of obstacles that interfere with the search and rescue work.

Real-time acquisition of compact volumetric maps

Building compact 3D maps of the environment models has become an important research topic. This paper presented an efficient stream decimation algorithm of massive meshes. The algorithm adapted the pre-processing step leading to lower in-corn memory consumption. This algorithm is applied to reconstructing compact terrain with mobile robot, achieving satisfying results.

Application of Three-dimensional Laser Scanning Technology in the Teaching Practice of Surveying and Mapping of Ancient Buildings

Based on the study of the application of three-dimensional laser scanning technology in ancient building surveying and mapping,this paper briefly describes the working principle and flow of three-dimensional laser scanning technology.Based on the practical application,this paper puts forward the discussion of related problems and matters needing attention.This has a certain reference significance for the study of new technology in surveying and mapping of ancient buildings.

基于UBmap/AHP/FAST的医院建筑火灾应急避难装置设计

为提高医院火灾应急避难能力,针对医院住院楼建筑,设计一款在火灾下能及时进行应急避难、安全逃脱的装置产品。集合用户行为地图(UBmap)、层次分析法(AHP)、功能分析系统技术法(FAST),融入整体设计构架中,探究应急避难装置产品的用户需求以及相关产品设计要素。将医院住院楼建筑环境特点及患者人群对火灾的逃生行为过程融入UBmap中,构建行为旅程图,推测并提取各个阶段需求指标;再通过AHP/FAST聚合方法,对需求指标进行重要度排序,并转化为主次功能进行分析求解,以此推断产品设计定位与功能逻辑的准确性,完成设计方案生产。综合UBmap/AHP/FAST为依据的理论模型,运用到医院住院楼建筑火灾下的避难装置设计开发流程中,并运用ANSYS Workbench软件有限元分析方案,进一步验证校核该设计的整体装置强度与可行性。结果表明:将不同的方法融合分析,系统性地对产品展开分析设计与实现且进行仿真检验,减少设计过程中的薄弱环节与不确定性问题,能够使设计更具系统性与科学性,达到降低避难装置的生产成本、实现安全效率化、防灾减灾的目标。

OSM Buildings在三维电子地图中的应用研究

三维建筑物是三维电子地图的重要组成要素,现有的网络地图普遍采用Web GL技术在地图上展现建筑物三维效果。本文则采用开源纯Java Script技术的OSM Buildings三维建筑物引擎实现在传统二维电子地图上添加三维建筑物并支持在浏览器中以无插件的方式浏览3D场景。全文详细叙述了三维建筑物所用底图数据的采集、校正、投影以及将底图数据转换成OSM Buildings所支持的Geo JSON格式的过程。

Multi-scale Representation of Building Feature in Urban GIS

This paper aims at multi_scale representation of urban GIS,presenting a model to dynamically generalize the building on the basis of Delaunay triangulation model.Considering the constraints of position accuracy,statistical area balance and orthogonal characteristics in building cluster generalization,this paper gives a progressive algorithm of building cluster aggregation,including conflict detection (where),object (who) displacement,and geometrical combination operation (how).The algorithm has been realized in an interactive generalization system and some experiment illustrations are provided.

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

Displacement of Building Cluster Using Field Analysis Method

This paper presents a field based method to deal with the displacement of building cluster, which is driven by the street widening. The compress of street boundary results in the force to push the building moving inside and the force propagation is a decay process. To describe the phenomenon above, the field theory is introduced with the representation model of isoline. On the basis of the skeleton of Delaunay triangulation, the displacement field is built in which the propagation force is related to the adjacency degree with respect to the street boundary. The study offers the computation of displacement direction and offset distance for the building displacement. The vector operation is performed on the basis of grade and other field concepts.

Camera Pose Estimation Using Collaborative Databases and Single Building Image

Cities are in constant change and city managers aim to keep an updated digital model of the city for city governance. There are a lot of images uploaded daily on image sharing platforms (as “Flickr”, “Twitter”, etc.). These images feature a rough localization and no orientation information. Nevertheless, they can help to populate an active collaborative database of street images usable to maintain a city 3D model, but their localization and orientation need to be known. Based on these images, we propose the Data Gathering system for image Pose Estimation (DGPE) that helps to find the pose (position and orientation) of the camera used to shoot them with better accuracy than the sole GPS localization that may be embedded in the image header. DGPE uses both visual and semantic information, existing in a single image processed by a fully automatic chain composed of three main layers: Data retrieval and preprocessing layer, Features extraction layer, Decision Making layer. In this article, we present the whole system details and compare its detection results with a state of the art method. Finally, we show the obtained localization, and often orientation results, combining both semantic and visual information processing on 47 images. Our multilayer system succeeds in 26% of our test cases in finding a better localization and orientation of the original photo. This is achieved by using only the image content and associated metadata. The use of semantic information found on social media such as comments, hash tags, etc. has doubled the success rate to 59%. It has reduced the search area and thus made the visual search more accurate.

Integrated GIS, Remote Sensing and Survey Data for Damage Assessment of Buildings in Tsunami Event, Ishinomaki City, Japan

The 2011 Tsunami event in the eastern coastal area of Japan caused a huge amount of damages or devastations on buildings. To this date, several field surveys have been conducted which provide detailed information about inundation areas and building damage characteristics in attacking east coastal areas by this tsunami. In this study, building damage data of Ishinomaki city, with special attention to the plain coast affected area, are classified and analyzed using data surveyed by the Ministry of Lands, Infrastructure and Transportation of Japan (MLIT) for more than 52,000 structures. The classification includes information on six levels of damage, four types of building materials and damages due to tsunami inundation for each building material which are necessary information for an effective hazard mitigation. Notably, damage level percentage distribution of different building materials is plotted for different inundation depth ranges in several sets of figures. This graphic illustration not only shows a better resistant performance of Reinforced Concrete (RC) and steel buildings over wood or other buildings for all inundation depth ranges, but also can explain clearly the inundation-induced damage behavior for each building material as well as the threshold depth for each damage level. Moreover, this research contains an analysis of vulnerable areas due to the coastal topography and the geographical factors. Surveyed data provided by Geospatial information authority of Japan (GSI) that classifies Ishinomaki plain coast area into three classes are compared with the damage map produced using an Analytical Hierarchy Process (AHP) methodology in ArcGIS 10.2 environment. The influence of key geographical features on tsunami-induced building damage, notably Kitakami river and water canals flooding, is taken into account with respect to the weighting of factors. A good agreement produced building damage map with surveyed GSI data shows the power of a GIS tool based on the AHP approach for tsunami damage assessment. The results of this study are useful to understand the damage behavior of buildings with different structural materials located in coastal areas vulnerable to the tsunami disaster.

A New Generation of Intelligent Mapping and Remote Sensing Scientific Test Satellite Luojia-301

With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites,they have become an important support for obtaining global accurate surveying and mapping remote sensing information.At present,optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities,but there is a serious lag problem in mapping and remote sensing information services.It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services.Firstly,based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics,this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites.Further,according to the application requirements of real-time,intelligence,and popularization,puts forward the design concept of integrated intelligent remote sensing satellite integrating communication,navigation,and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite.Then expounds on the performance and characteristics of the Luojia-301 satellite,a new generation of intelligent surveying and mapping remote sensing scientific test satellite.And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites.Luojia-301 satellite integrates remote sensing and communication functions.It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet,which is of great significance to the construction of China’s spatial information network.

铁路三维电子沙盘搭建及其应用研究

为解决目前铁路行业电子沙盘存在的表达方式不一、应用内容单一、数据泄露风险大、数据加载效率低等问题,研究海量地理地质及BIM模型的数据处理、管理和应用方法,建立以BIM+GIS为技术核心的标准化电子沙盘,从而更好地服务于铁路设计和建造。根据鲁南高铁、拉林铁路、成渝中线高铁等项目实践总结,在中国铁路BIM联盟和国铁集团相关标准基础上提出了面向电子沙盘应用的地形、影像、BIM模型、专题图的数据制作标准,从坐标转换、数据转换、数据切片、知识图谱建立、模型发布、前端数据汇集的方法完成了电子沙盘的搭建。最后,在电子沙盘中实现了百公里级、多专业数据的在线高效组织和管理,提出的方法和标准适用于铁路设计、建设过程的管理和分析应用。

长城色彩折射的建造技艺、管理制度与民族智慧

与宫殿、寺庙、石窟等建筑遗产相比,长城在材料色彩方面“乏善可陈”,甚至还出现专有名词“长城灰”,将长城的色彩特征固化。本文通过无人机采集明长城全线图像并引入图像类人工智能提取其主色调,首次量化各段长城色彩、绘制全线色彩地图,证明长城“富彩”;并结合材料成分分析、史料挖掘,首次揭示色彩背后蕴含的长城材料色彩法则、色彩质量评价标准与奖励办法,以及以色立威、以建止战的核心意图。这一成果尝试填补古代建筑色彩研究的长城缺环、揭示长城作为军事遗产的材料色彩艺术造诣,挖掘出未开发长城段新的遗产景观资源,从全新的视角助推长城国家文化公园建设。

中国BIM体系下装配式建筑可视化图谱分析

为有效解决建筑工业化和智能化协同发展所面临的问题,中国正积极促进建筑信息模型(Building Information Modeling,BIM)支持下装配式建筑的发展。运用了文献计量分析方法,对2011—2021年中国BIM体系下装配式建筑的研究趋势、作者和机构集群及研究热点关键词进行了可视化图谱分析,旨在提高建筑从业者对BIM体系下装配式建筑的综合认知。研究发现:跨地域机构研究承接协同、“装配式建筑-BIM-EPC”一体化及运营维护的全生命周期设计管理是未来的重点研究方向。

三维激光扫描技术在历史建筑测绘中的应用——以闽清县历史建筑测绘建档项目为例

历史建筑作为城市的文脉,承载着一座城市的历史,受各种因素影响遭受不断的侵蚀甚至灭失,其保护形势越来越严峻。文章以闽清县历史建筑保护测绘为例,采用三维激光扫描技术,结合无人机倾斜摄影技术,对历史建筑真彩色三维点云模型建设进行了探索研究,为历史建筑的测绘资料建档和文物保护工作积累了宝贵的技术经验。结语对该技术进行了总结分析,认为三维激光扫描技术不仅能大幅提升工作效率和测量成果精度,还可实现历史建筑三维可视化,具有广阔的应用前景。

文献计量统计下装配式建筑热点与未来发展路径综述

为分析目前装配式建筑研究热点与发展态势,运用Citespace与Vosviewer软件进行文献计量统计与可视化分析,得到装配式建筑研究领域科学知识图谱及时空分布规律。结果表明:我国装配式建筑研究领域核心学者共46位,中国建筑科学研究院、东南大学、同济大学等在该领域拥有较高的发文数量和影响力;国外研究领域核心学者61位,英国、美国、澳大利亚等国家的大学和科研机构是该领域的重要贡献者;21世纪以来装配式建筑领域发展迅速,研究主题涉及模块化设计、构件生产与制造、施工技术与过程、质量控制与管理等。分析国内外装配式建筑的研究及发展趋势,可为我国装配式建筑发展提供文献理论参考和数据支撑。