地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1383-1393.doi: 10.12082/dqxxkx.2020.190472

• 数据共享与数据挖掘 • 上一篇    下一篇

基于边云协同的AR空间分析计算框架

别勇攀, 关庆锋*(), 姚尧   

  1. 中国地质大学(武汉)地理与信息工程学院,武汉 430078
  • 收稿日期:2019-08-27 修回日期:2019-11-29 出版日期:2020-06-25 发布日期:2020-08-25
  • 通讯作者: 关庆锋 E-mail:guanqf@cug.edu.cn
  • 作者简介:别勇攀(1996— ),男,湖北天门人,硕士生,研究方向为地理数据AR可视化、城市计算。E-mail: yongpanbie@cug.edu.cn
  • 基金资助:
    国家自然科学基金项目(41671408);国家自然科学基金青年基金项目(41801306);国家自然科学基金青年联合基金项目(U1711267);湖北省自然科学基金杰出青年项目(2017CFA041)

A Framework for AR Spatial Analysis based on Edge-Cloud Integration

BIE Yongpan, GUAN Qingfeng*(), YAO Yao   

  1. School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
  • Received:2019-08-27 Revised:2019-11-29 Online:2020-06-25 Published:2020-08-25
  • Contact: GUAN Qingfeng E-mail:guanqf@cug.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41671408);Youth Fund Project of National Natural Science Foundation of China(41801306);Youth joint fund project of National Natural Science Foundation of China(U1711267);Natural Science Fund of Hubei Province(2017CFA041)

摘要:

地理数据的增强现实(AR)技术,是地理信息可视化的重要发展方向之一。受限于头戴式AR设备有限的计算能力,已有的地理信息AR研究多集中于地理信息的可视化,并未提供复杂的空间分析计算功能。本文提出了一套基于边缘计算与云计算协同的AR空间分析计算框架。AR端完成空间数据的可视化、人机交互、数据预处理等;云端则完成复杂的空间分析计算任务以及空间数据的持久化存储;边云二元存储方法和三维可视化模型-三维地理模型间的坐标转换模块完成AR端和云端间的高效链接;从而达到高效率的、端对端的协同计算。基于该框架,本文利用头戴式AR设备Hololens,并基于武汉市的建筑物三维数据和天空可视因子算法,实现了在AR可视化的同时进行复杂的空间计算。结果表明,在保证空间分析计算效率和精度的同时,Hololens性能表现稳定,三维空间可视化的体验良好(FPS约35),证明了边云协同框架下的AR在空间分析计算具有广阔前景。

关键词: 增强现实技术(AR), 边缘计算, 云计算, 边云协同, Hololens, 空间分析, 地理数据

Abstract:

Augmented Reality (AR) for geographic data is an important development direction of geographic information visualization. In recent years, some geographic studies have begun to use head-wear AR devices to visualize geographic data, making geographic spatial information more fully expressed. However, due to the limited computing power of head-wear AR devices, most of existing geographic information AR studies focus on the visualization of geographic information, yet do not provide complex spatial analysis and computation functions. This paper presents a framework for spatial analysis on AR devices by edge-cloud integration. In order to achieve efficient and end-to-end collaborative computation, the AR device is responsible for spatial data visualization, human-computer interaction, and data preprocessing; while the cloud server is responsible for complex spatial analysis tasks and persistent storage of spatial data. The binary storage method of edge-cloud and the coordinate conversion module between the 3D visualization model and the 3D geographic model provide efficient links between AR end and cloud end. Specifically, the framework divides three-dimensional geospatial data into three-dimensional AR visualization model data and three-dimensional geographic model data with attribute information, which are stored in AR and the server, respectively. The coordinates of the three-dimensional AR visualization model and geospatial coordinates are mapped using Bursa-wolf seven-parameter coordinate transformation method. Finally, HTTP protocol is used to transmit data to achieve high efficiency and end-to-end collaborative computing. Based on this framework, this paper used Hololens, a head-mounted AR device, and based on three-dimensional building data in Wuhan and a sky visibility factor algorithm, to realize complex spatial computation in the meantime of AR visualization. The findings suggest that our framework can provide smooth and stable 3D visualization (FPS was about 35) while guaranteeing the efficiency and accuracy of the complex sky-view factor computation. In conclusion, the edge-cloud integration technology can enable AR devices to perform complex spatial analysis and computation.

Key words: Augmented Reality, edge computing, cloud computing, edge-cloud integration, hololens, spatial analysis, geographic data