摘要
实时构建地表环境高逼真数字模型一直是遥感航测领域的研究热点,也是实现地理环境的虚拟映射并进而形成数字孪生地理环境的关键基础.针对当前地表环境三维数字模型构建中存在的速度慢、时效性低和大场景应用受限等问题,本文在综述现有国内外研究的基础上,结合团队研究基础,提出一个基于无人机平台的地表环境实时稠密点云生成与数字模型构建方法.研究通用实时定位与地图构建框架,打通了从数据获取到三维点云生成、数字模型重建、结果分析等技术链条,设计动态内存管理模块和联合GNSS优化的PI-SLAM,突破了在线获取数据与位姿解算、实时稠密点云生成、实时数字表面模型(DSM)和数字正射影像图(DOM)构建等关键技术.经实验验证,方法在稠密点云生成和数字模型构建精度与现有离线建模算法相当的同时,速度提升了30-50倍,达到在线采集数据并实时建模的程度.应用案例表明,本方法可应用于灾害预警救援、应急管理、作战模拟等高时效地形三维重建中,同时能够为数字孪生地理环境数字底座构建提供技术支撑.
Abstract
The development of high-fidelity 3D digital model of the land surface environment in real time has become essential in many fields,including urban planning,agricultural surveying and mapping,disaster management,and military applications.Building a high-fidelity digital model of the land surface environment in real time is the key foundation for realizing the virtual mapping of the geographic environment and then forming a digital twin geographic environment.However,current methods for constructing these models suffer from several challenges such as slow speed,low timeliness,and limited application in large scenarios.To overcome these challenges,this paper proposes a new real-time dense point cloud generation and digital model construction method based on unmanned aerial vehicle(UAV)platforms.The proposed algorithm remarkably improves the speed and accuracy of land surface model construction compared with existing algorithms and improves the degree of online data collection and real-time modeling.The algorithm is based on a general simultaneous localization and mapping framework.The technical chain from data acquisition,data processing,feature extraction and matching to 3D point cloud generation,digital model reconstruction,and result analysis is unobstructed.The algorithm breaks through the key technologies in scene reconstruction such as online data acquisition and pose problem solving,real-time dense point cloud generation,digital surface model,and digital orthophoto map construction.According to the experimental results of the dataset containing different land surface environments such as cities,farmland,mountains,and deserts,the algorithm proposed in this paper can process high-quality land surface environment dense point clouds and digital models while being 30-50 times faster than existing algorithms such as Pix4DMapper.On average,it can process a high-resolution image in less than 1 second,whereas the interval for general aerial survey drones is 1-2 seconds.Furthermore,the application of the algorithm proposed in this paper to emergency rescue work of mountain flood disasters can assist in the real-time reconstruction of inaccessible areas,survey the area and location of washed-out and sediment areas,and support emergency rescue work.The proposed real-time dense point cloud generation and digital model construction method based on UAV platforms represents a considerable advancement in the field of geographic information systems.It can revolutionize various geographic fields,including disaster warning and management,emergency response,military applications,and agricultural and urban planning.Additionally,due to the breakthrough of the algorithm,it solves the core problem of real-time mapping from reality to virtual in the construction of digital twins in geographic environments.Therefore,it can empower digital twins and provide a digital foundation for digital twin applications in geographic environments.Based on the real-time mapping of 3D information of the ground environment,parallel simulation and decision making can be conducted.Therefore,it is also expected to achieve functions such as emergency warning,scheme evaluation,and decision optimization based on the dynamic monitoring of target information in the scene,further improving the automation and intelligence level of the applied system.
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