Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

65
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...
65
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

74
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
74
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

289
Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
289
Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

1.4K
Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
1.4K
Rapidly Varying Flow01:24

Rapidly Varying Flow

62
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
62
Plane Potential Flows01:23

Plane Potential Flows

388
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
388

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Human Fall Detection Based on Body Posture Spatio-Temporal Evolution.

Sensors (Basel, Switzerland)·2020
查看所有相关文章

相关实验视频

Updated: Jul 3, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

16.6K

基于卡尔曼的场景流量估计用于动态场景中的点云密集和3D对象检测.

Junzhe Ding1, Jin Zhang1, Luqin Ye1

  • 1School of Rail Transportation, Soochow University, Suzhou 215500, China.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括

本研究介绍了一种基于卡尔曼的方法,通过在动态场景中加密点云来改进3D对象检测. 这种方法纠正了局部化错误,提高了 LiDAR 系统的形状完整性和检测精度.

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
  • 3D 感知 3D 感知

背景情况:

  • 点云密集对于3D环境的理解和对象检测等任务至关重要.
  • 由于点云不完整和变形,现有的注册方法在动态目标中失败.
  • 场景流量估计中的定位错误阻碍了准确的3D感知.

研究的目的:

  • 开发一种基于卡尔曼的新场景流量估计方法,用于点云密集.
  • 为了提高动态场景中的3D对象检测精度.
  • 在动态场景流量估计中解决和纠正本地化错误.

主要方法:

  • 整合了卡尔曼波器,在场景流量估计过程中纠正动态目标位置.
  • 该方法估计了长序幕场景流动,减轻了累积定位错误.
  • 点云密度通过准确的场景流量估计来实现.

主要成果:

  • 显著提高了仅使用LiDAR的3D物体探测器的性能.
  • 增强了动态目标的形状完成的准确性和精度.
  • 与基线方法相比,KITTI 3D追踪数据集的优异结果.
关键词:
3D对象检测检测 3D对象检测卡尔曼过器可以过.点云的密集化和点云的密集化场景流量估计 场景流量估计

更多相关视频

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

相关实验视频

Last Updated: Jul 3, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

16.6K
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

结论:

  • 提出的基于卡尔曼的方法有效地处理点云密集的动态场景.
  • 它克服了现有的基于注册的方法的局限性.
  • 该技术提供了一个强大的解决方案,用于在复杂的,动态的环境中准确的3D对象检测.