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Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation.

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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基于机器学习策略的3D道路边界提取,使用LiDAR和图像衍生的MMS点云.

Baris Suleymanoglu1, Metin Soycan1, Charles Toth1

  • 1Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Ave., Columbus, OH 43210, USA.

Sensors (Basel, Switzerland)
|January 23, 2024
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概括

本研究引入了一种新的方法,可以从点云数据中精确地提取道路边界,为各种道路类型和绘图系统实现高精度. 该算法有效地提取路边,对于自动驾驶和高清地图生成等应用至关重要.

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3D道路开采 3D道路开采路边检测 路边检测机器学习是机器学习.移动激光扫描 移动激光扫描移动地图系统移动地图系统

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科学领域:

  • 地理学工程 工程地质学
  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 准确的道路边界提取对于基础设施数据至关重要,支持自动驾驶,导航和高清地图生成.
  • 现有的方法面临着各种道路类型和复杂的城市环境的挑战.
  • 基于图像的点云数据为道路开采提供了丰富的来源,但需要强大的算法.

研究的目的:

  • 从基于图像的点云数据中开发一种普遍适用的道路边界和路边抽取方法.
  • 整合DBSCAN和RANSAC以提高道路开采性能.
  • 通过不同的移动映射系统 (MLS和MMS) 验证该方法的有效性.

主要方法:

  • 集成DBSCAN (基于密度的应用程序与噪音的空间聚类) 和RANSAC (随机样本共识) 算法.
  • 从移动LiDAR系统 (MLS) 和基于光谱的移动绘图系统 (MMS) 获取的点云数据的处理.
  • 使用手动测量的参考道路边界数据进行评估,重点关注完整性,正确性和整体质量指标.

主要成果:

  • 两个数据集的完成率为93.2%和84.5%.
  • 记录的正确率为98.6%和93.6%,对于各自的数据集.
  • 证明了93.9%和84.5%的道路道整体提取质量,准确处理复杂的城市环境和各种数据源.

结论:

  • 提出的方法提供了一种开创性和有效的方法,用于从基于图像的点云中提取道路信息.
  • 该算法证明了对具有不同数据特征的多种移动映射系统的稳定性和适用性.
  • 即使在混乱的城市环境中,也可以精确地提取直线和曲的道路边界和路边.