共查询到20条相似文献，搜索用时 31 毫秒
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Hanjiang Xiong Wei Ma Jianya Gong Douadi Abdelalim 《International Journal of Digital Earth》2019,12(5):525-543
Realistic texture mapping and coherent up-to-date rendering is one of the most important issues in indoor 3-D modelling. However, existing texturing approaches are usually performed manually during the modelling process, and cannot accommodate changes in indoor environments occurring after the model was created, resulting in out-dated and misleading texture rendering. In this study, a structured learning-based texture mapping method is proposed for automatic mapping a single still photo from a mobile phone onto an already-constructed indoor 3-D model. The up-to-date texture is captured using a smart phone, and the indoor structural layout is extracted by incorporating per-pixel segmentation in the FCN algorithm and the line constraints into a structured learning algorithm. This enables real-time texture mapping according to parts of the model, based on the structural layout. Furthermore, the rough camera pose is estimated by pedestrian dead reckoning (PDR) and map information to determine where to map the texture. The experimental results presented in this paper demonstrate that our approach can achieve accurate fusion of 3-D triangular meshes with 2-D single images, achieving low-cost and automatic indoor texture updating. Based on this fusion approach, users can have a better experience in virtual indoor3-D applications. 相似文献
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高精度三维测图是室内三维制图的重要支撑,基于三维激光雷达扫描技术的三维测图成本高,需要提前布置标靶,在室内复杂环境中易导致数据不完整;基于图像序列的三维重建建模时间长,易受多种因素影响。针对以上问题,本文将RGB-D SLAM技术应用于室内高精度三维测图中。通过将深度相机与SLAM技术相结合,计算相机位姿并恢复三维空间信息,获取室内三维点云模型,并以目标物实际量测为基准评价密集点云精度。试验结果表明,该方法可快速获取精度较高的三维点云模型,成本低且效率高,能够较好地满足应用需求。 相似文献
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Jinjin Yan Abdoulaye A. Diakit Sisi Zlatanova Mitko Aleksandrov 《Transactions in GIS》2020,24(2):371-389
Outdoor navigation is widely used in daily life, but faces various issues related to the fidelity of outdoor navigation networks. For instance, agents (pedestrians) are often guided via unrealistic detours around places without clear paths (e.g., squares) or if there are vertical constraints such as overpasses/bridges. This is partly explained by the fact that the main sources of navigation networks in current outdoor navigation are two‐dimensional road/street networks. Utilizing a three‐dimensional space‐based navigation model, compatible with some indoor approaches, is a popular way to address the above‐mentioned issues. A 3D space‐based navigation model is generated by treating 3D spaces as nodes and the shared faces as edges. Inputs of this model are enclosed 3D spaces (volumes). However, outdoor spaces are generally open and unbounded. This article puts forward an approach to enclose outdoor spaces and mimic the indoor environments to derive a network based on connectivity and accessibility of spaces. The approach uses 2.5D maps and consists of three major steps: object footprint determination, footprint classification and space creation. Two use cases demonstrate the proposed approach. Enclosing outdoor spaces opens a new research direction toward providing seamless indoor/outdoor navigation for a range of agents. 相似文献
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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。 相似文献
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介绍了基于机器视觉的数字摄影测量智能化新方法：移动激光雷达瞬时三维构像方法。该方法作为移动激光雷达瞬时三维构像理论,解决了无GPS辅助的移动激光雷达自定位与瞬时构像问题,使移动激光雷达灵活应用在室内、室外等多种环境。首先,分析了瞬时构像方法与传统构像方法的不同,以及现有方法的可移植性;接着,给出了视准轴误差自定标方法、基于平整度的有效特征提取算法和快速优化平差算法,构建出移动激光雷达自定位与瞬时构像的核心技术框架;最后,通过3种不同环境构像试验验证本文方法的有效性和适应性。研究表明,该方法可应用在结构化环境与非结构化环境、室内与室外环境,可水平构像也可垂直构像,且无需后数据处理瞬时构像。 相似文献
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Ramesh Kestur Akanksha Angural Bazila Bashir S. N. Omkar Gautham Anand M. B. Meenavathi 《Journal of the Indian Society of Remote Sensing》2018,46(6):991-1004
UAVs are fast emerging as a remote sensing platform to complement satellite based remote sensing. Agriculture and ecology is one of the important applications of UAV remote sensing, also known as low altitude remote sensing (LARS). This work demonstrates the use and potential of LARS in agriculture, particularly small holder open field agriculture. Two UAVs are used for remote sensing. The first UAV is a fixed wing aircraft with a high spatial resolution visible spectrum also known as RGB camera as a payload. The second UAV is a quadrotor UAV with an RGB camera interfaced to an on-board single board computer as the payload. LARS was carried out to acquire aerial high spatial resolution RGB images of different farms. Spectral–spatial classification of high spatial resolution RGB images for detection, delineation and counting of tree crowns in the image is presented. Supervised classification is carried out using extreme learning machine (ELM), a single hidden layer feed forward network neural network classifier. ELM was modelled for RGB values as input feature vectors and binary (tree and non-tree pixels) output class. Due to similarities in spectral intensities, some of the non-tree pixels were classified as tree pixels and in order to remove them, spatial classification was performed on the image. Spatial classification was carried out using thresholded geometrical property filtering techniques. Threshold values chosen for carrying out spatial classification were analysed to obtain optimal values. Finally in the delineation and counting, the connected tree crowns were segmented using Watershed algorithm performed on the image after marking individual tree crowns using Distance Transform method. Five representative UAV images captured at different altitudes with different crowns of banana plant, mango trees and coconut trees were used to demonstrate the performance of the proposed method. The performance was compared with the traditional KMeans spectral–spatial method of clustering. Results and comparison of performance parameters of KMeans spectral–spatial and ELM spectral–spatial classification methods are presented. Results indicate that ELM performed better than KMeans. 相似文献
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ABSTRACTThe recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applications in the fields of Augmented Reality (AR), robotics, indoor GIS and self-driving. Camera localization is often a key and enabling technology among these applications. In this paper, we developed a novel camera localization workflow based on a highly accurate 3D prior map optimized by our RGB-D SLAM method in conjunction with a deep learning routine trained using consecutive video frames labeled with high precision camera pose. Furthermore, an AR registration method tightly coupled with a game engine is proposed, which incorporates the proposed localization algorithm and aligns the real Kinetic camera with a virtual camera of the game engine to facilitate AR application development in an integrated manner. The experimental results show that the localization accuracy can achieve an average error of 35?cm based on a fine-tuned prior 3D feature database at 3?cm accuracy compared against the ground-truth 3D LiDAR map. The influence of the localization accuracy on the visual effect of AR overlay is also demonstrated and the alignment of the real and virtual camera streamlines the implementation of AR fire emergency response demo in a Virtual Geographic Environment. 相似文献
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针对即时定位与地图构建(SLAM)在室内动态环境下定位精度低和地图效果差的问题,提出一种基于几何约束和目标检测的室内动态SLAM方法. 使用目标检测网络获取语义信息,提出运动物体漏检的方法;根据先验知识,提出准确识别动态区域的信息判定方法;结合几何约束和深度学习方法剔除动态点,利用静态点估计相机位姿;根据存储信息构建可闭环的静态地图. 在TUM数据集上进行实验,定位精度比ORB-SLAM2提高97.5%,相较于其他动态SLAM可取得更好的性能. 在室内真实环境进行实验,构建的静态地图更准确,有效提高了室内动态SLAM的定位精度和地图效果. 相似文献
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无人机视觉SLAM协同建图与导航 总被引：1，自引：0，他引：1
地面机器人由于其工作空间的限制,对环境感知存在较大局限性,结合空中机器人在视角方面的优势,实现空地机器人协作是主流趋势。本文提出了一种基于无人机视觉SLAM的协同建图与导航方法,利用无人机空中视角带来的大范围环境感知能力,协助地面机器人快速构建环境模型,提高地面机器人在具有挑战性的未知环境中建图与导航能力。本文方法首先构建了一个显著闭合边界实时检测和跟踪线程,结合点、线特征以及显著闭合边界,提出一种视觉SLAM方法用于无人机空中建图,与传统方法相比,闭合边界的结合极大优化了建图的效果。其次,地面机器人根据无人机获得的初始全局地图自动规划全局路径,在移动过程中,利用搭载的激光传感器对无人机建的初始地图进行更新,并且对路径进行连续的重新规划,避免与障碍物发生碰撞。为了验证本文方法的可行性和先进性,分别进行了仿真试验和真实试验。试验结果表明,本文方法显著提高了建图效果,实现了协同建图与导航方法的完整过程,提高了地面机器人在具有挑战的未知区域中进行自主建图和导航的能力。但是本文方法在障碍物分布密集、地面高低起伏等复杂情况下效果不佳,且实现的二维导航局限性大,未来工作立足于融合激光雷达、IMU等多传感器,提高深度估计和位姿估计等任务效果用于构建精准的三维占据栅格地图,并进一步设计三维空间空地协同建图与导航的方法。 相似文献
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传统数字正射影像图(DOM)采用微分纠正方法消除相机倾斜与地形起伏所带来的投影误差,但会产生双重投影现象. 真数字正射影像(TDOM)可有效解决DOM的双重投影问题. 文中提出一种基于无人机影像的实景三维模型TDOM制作方法, 首先通过无人机挂载五镜头相机获取航空摄影测量影像并生成实景三维模型;然后采集实景三维模型建筑物顶部轮廓边界线,并构建其三维体模型;最后基于建筑物顶部轮廓边界线三维体模型进行遮蔽检测和遮蔽补偿生成TDOM. 以云南师范大学呈贡校区无人机影像实验数据验证了本文方法,实验表明本文的方法可作为一种生成高质量的TDOM方法. 相似文献
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由于三维场景与二维图像之间存在着非线性和高度复杂的关系,使用相机对用户的位置进行估计需要建立复杂的数学模型。针对该问题,本文提出了使用神经网络估计的单相机进行室内定位的方法。室内定位系统的主要优点是LED能够使用可见光通信发送其位置信息。首先,该方法充分利用LED光线的投影不变性,借助图像传感器通信（ISC）完成虚拟直线的构建;然后,运用神经网络估计从该虚拟直线中提取出相机的方向信息;最后,使用一个简单数学方程估计用户位置。仿真试验考虑了4种情形,结果表明,本文提出的方法性能优于同类方法,对于一个房间内的大部分地方,定位误差在35 mm以内。 相似文献
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提出了一种利用天文观测图像对卫星及其子部件三维姿态进行估计的方法。该方法利用等积剖分格网建立多视角姿态模型数据库,利用基于混合噪声的最大似然算法对观测图像进行图像复原与滤波等预处理,然后进行分割和几何特征提取,提出了一种尺度不变的特征检索方法对卫星几何特征进行编码,并在姿态数据库中进行最小距离匹配,最终估计出空间目标的三维姿态。实验结果验证了本文姿态估计方法的有效性和准确性。 相似文献
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当前室内三维建模的纹理映射工作主要依赖人工完成,工作量大、处理效率低,且对建模后室内环境的变更难以实现及时更新,导致室内模型纹理存在不真实甚至错误的现象。对此,提出一种室内三维模型纹理手机图片的自动更新方法,该方法主要以手机拍摄的图片为数据源,首先利用智能手机传感器获取拍摄图片的位姿参数,进而通过室内场景理解技术提取图片空间中的三维布局结构,最后与几何模型进行坐标点匹配解算纹理坐标,实现手机图片到三维几何模型的自动纹理映射。实验结果表明,该方法能够利用手机图片数据实现室内环境下的三维模型纹理自动生成及动态更新,有效提高室内三维场景表达的真实性。 相似文献
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王桂钢 《测绘与空间地理信息》2018,(2):106-108,112
以合作市实景三维建模为例,阐述了无人机倾斜摄影、实景三维模型数据生产的基本原理。利用无人机搭载倾斜相机的方式采集影像数据,选用ContextCapture软件作为数据处理平台,将从影像中获得的纹理信息和几何模型相结合,构建合作城区附近24 km~2的实景三维模型。 相似文献
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轻小型无人机测绘遥感系统研究进展 总被引：1，自引：0，他引：1
地球空间信息是人工智能、大数据时代的重要数据基础,轻小型无人机测绘遥感技术作为中国当前和未来获取厘米级分辨率、实时响应遥感数据的主要手段,必将发挥更加重要的作用。本文首先介绍了固定翼、多旋翼、无人直升机以及飞行控制系统、地面监控系统和遥控遥测链路的发展现状和潜在发展趋势;其次重点研究了数码相机、视频摄像机、倾斜相机、激光雷达、合成孔径雷达和定姿定位系统的利用现状和发展趋势;然后总结分析了当前无人机测绘遥感面临的系统检测、大范围实时遥感和遥感大数据精准解译方面的问题和挑战;最后面向人工智能、大数据、物联网、云计算等技术背景给出了轻小型无人机测绘遥感技术在飞行控制智能化、测绘遥感作业智能化和实时、实景无人机遥感技术应用模式创新等方面的发展趋势。 相似文献