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相关概念视频

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

Design Example: Alignment of a Road Line Using GIS

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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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Coordinates and Map Projections01:29

Coordinates and Map Projections

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Coordinates and map projections are essential tools in accurately representing the Earth's surface for various applications, ranging from navigation to spatial analysis. The latitude and longitude coordinate system is a universally recognized framework for defining locations. Latitude specifies the distance of a point north or south of the equator, measured in degrees from 0° at the equator to 90° at the poles. Longitude indicates a location's position east or west of the prime meridian,...
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Plotting of Topographic Maps01:29

Plotting of Topographic Maps

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Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
39
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Centroid for the Paraboloid of Revolution01:16

Centroid for the Paraboloid of Revolution

526
The paraboloid of revolution is an axially symmetric surface generated by rotating a parabola around its axis. This shape has several applications in mechanical engineering due to its advantageous structural properties, such as strength against stress concentration points and rotational symmetry.
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Profile Leveling and Cross Sections01:26

Profile Leveling and Cross Sections

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Profile leveling and cross-sections are surveying methods used to determine and document terrain elevations for infrastructure projects such as highways, railroads, canals, and pipelines. These methods provide data for earthwork planning and alignment of proposed routes.  Profile leveling involves measuring elevations along a fixed line to create a vertical terrain profile. A surveyor sets up a leveling instrument at the benchmark (BM) and records a backsight (BS) to determine the...
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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点云墙投影用于现实的道路数据增强.

Kana Kim1, Sangjun Lee2, Vijay Kakani3

  • 1Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea.

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概括
此摘要是机器生成的。

这项研究引入了一个新的框架,用于从遥远的物体生成精确的合成LiDAR点,这对于先进的驾驶辅助系统 (ADAS) 至关重要. 该方法提高了3D对象检测的准确性,而不需要过多的计算资源.

关键词:
李达尔 (LiDAR) 是一种激光雷达.数据增强数据增强对象检测检测对象检测对象检测一个点云,一个点云.综合数据 综合数据

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科学领域:

  • 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
  • 自动驾驶自动驾驶的自动驾驶

背景情况:

  • 从LiDAR数据生成合成对象点对于高级驾驶辅助系统 (ADAS) 至关重要.
  • 使用稀疏的LiDAR数据精确地从遥远的物体上生成点仍然是一个重大挑战.
  • 现有的方法往往需要大量的计算能力.

研究的目的:

  • 提出一种用于生成高精度合成LiDAR点的新框架,特别是从遥远的物体.
  • 为了解决当前合成数据生成技术的计算强度限制.
  • 在ADAS应用中提高3D物体检测模型的准确性.

主要方法:

  • 一个由三个模块组成的框架:位置确定,对象生成和合成注释.
  • 使用球形点跟踪方法来增强3D LiDAR远距离的物体.
  • 采用点云对象投影和点墙生成,用于诸如集队等场景的姿势确定.
  • 使用多个LiDAR系统增强远点描述.

主要成果:

  • 该框架在KITTI数据集上使用3D检测模型进行了评估:PointPillars,PV-RCNN和Voxel R-CNN.
  • 显示了平均平均精度 (mAP) 的提高:PointPillars的1.97%,PV-RCNN的1.3%,Voxel R-CNN的0.46%.
  • 在检测遥远物体和处理复杂场景方面取得了更好的性能.

结论:

  • 拟议的框架有效地从遥远的物体上产生精确的合成LiDAR点.
  • 它为ADAS应用程序的现有方法提供了一个计算效率高的替代方案.
  • 该框架显示了提高3D物体检测模型性能的巨大潜力.