整合 NoSQL、希尔伯特曲线和 R*-Tree 以高效管理移动激光雷达点云数据

IF 2.8 3区 地球科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ISPRS International Journal of Geo-Information Pub Date : 2024-07-14 DOI:10.3390/ijgi13070253
Yuqi Yang, Xiaoqing Zuo, Kang Zhao, Yongfa Li
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引用次数: 0

摘要

光探测与测距(LiDAR)技术的广泛应用导致三维点云数据激增,但也给数据存储和索引带来了挑战。高效的激光雷达数据存储和管理是各种基于激光雷达的科学应用进行数据处理和分析的先决条件。传统的关系数据库管理系统和集中式文件存储很难满足海量点云数据的存储、扩展和特定查询要求。然而,以可扩展性、速度和成本效益著称的 NoSQL 数据库提供了一种可行的解决方案。本研究提出了一种整合了希尔伯特曲线、R*树和B+树的移动激光雷达点云数据三维点云索引策略,从以下几个方面支持基于MongoDB的点云存储和查询:(1) 使用自适应空间分区策略对点云进行分区,以提高 I/O 效率并确保数据的本地性;(2) 使用希尔伯特曲线对分区进行编码,以构建全局索引;(3) 为每个点云分区构建局部索引(R*树),以便 MongoDB 能够原生支持点云数据的索引;以及 (4) 基于分层索引结构设计面向 MongoDB 的存储结构。我们评估了分块点云数据存储与MongoDB在空间查询方面的功效,发现与许多主流点云索引策略和数据库系统相比,建议的存储策略提供了更高的数据编码、索引构建和检索速度,以及更具可扩展性的存储结构,以支持高效的点云空间查询处理。
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Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data
The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.
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来源期刊
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information GEOGRAPHY, PHYSICALREMOTE SENSING&nb-REMOTE SENSING
CiteScore
6.90
自引率
11.80%
发文量
520
审稿时长
19.87 days
期刊介绍: ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.
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