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相关概念视频

Differential Leveling01:12

Differential Leveling

221
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
221
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
741
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

70
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
70
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

101
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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深 Voxelized 功能地图用于自动驾驶中的自我定位.

Yuki Endo1, Shunsuke Kamijo2

  • 1Department of Information & Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 153-8505, Japan.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括

这项研究引入了voxelized深度功能地图,以实现更准确的自动驾驶自我定位. 与传统的点云地图相比,这种新的地图格式提供了更高的效率和更少的存储需求.

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
  • 人工智能的人工智能

背景情况:

  • 自动驾驶依赖于精确的车道水平自我定位.
  • 传统的点云地图是数据密集型和冗余的.
  • 直接使用深度特征进行映射可以降低大规模环境中的性能.

研究的目的:

  • 提出一种新且实用的地图格式,用于使用深度特征进行自我定位.
  • 为了提高自动驾驶汽车定位的准确性和效率.
  • 解决现有的地图表示的局限性.

主要方法:

  • 开发声化深度特征地图,其中深度特征在小区域内定位.
  • 实现了一种自我定位算法,其中包括每声素残留和扫描点重新分配.
  • 对点云地图和标准特征地图进行比较实验分析.

主要成果:

  • 拟议的声音化深度特征地图实现了卓越的自我定位准确性.
  • 新的地图格式显示了提高效率和减少存储需求.
  • 实验验证证了每声素优化方法的有效性.

结论:

关键词:
自动驾驶自动驾驶的自动驾驶.深度学习是一种深度学习.自我本地化的自我本地化

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  • 语音化深度特征地图代表了车道级自我定位的实用和有效解决方案.
  • 这种方法比现有的自动驾驶方法有了显著的进步.
  • 提出的方法平衡了准确性,效率和存储需求.