Jove
Visualize
联系我们

相关概念视频

Differential Leveling01:12

Differential Leveling

189
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...
189
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

77
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
77
Leveling Equipment01:18

Leveling Equipment

89
As leveling involves measuring vertical distances relative to a horizontal line of sight, it requires a graduated rod, called a level rod, for vertical measurements and an instrument called a level for a horizontal sight line. A level includes a high-powered telescope with a mechanism for leveling to ensure the line of sight is horizontal when the bubble in the spirit level is centered. Leveling rods, made of wood, metal, or fiberglass, are graduated in feet or meters and commonly used in two-...
89

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

Spatiotemporally programmed nanomedicine engineering to resolve conflicting immunosignals in triple-negative breast cancer.

Signal transduction and targeted therapy·2026
Same author

Reprogrammed apoptotic platelets drive rapid hemostasis through phosphatidylserine and prostaglandin E2 signaling in preclinical models.

Science translational medicine·2026
Same author

Zwitterionic Lipid Nanotherapeutics from Mulberry for Oral Treatment of Diabetic Colitis.

ACS nano·2026
Same author

In situ self-assembled glycopeptide regulates tumor microenvironment for tumor immunotherapy.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Lysosome-targeting chimeras enable targeted protein degradation.

Cell chemical biology·2026
Same author

Conformation-programmed DNA computing.

Science advances·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章
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

相关实验视频

Updated: Jul 11, 2025

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

10.8K

数据驱动摄像机-LiDAR校准在数据收集车辆中的多层次优化.

Zijie Jiang1, Zhongliang Cai1, Nian Hui1

  • 1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的摄像机-LiDAR校准模型,可以克服用于准确数据融合的初始参数不佳. 该方法在各种环境中提高了效率和准确性,使自主系统受益.

关键词:
自动校准自动校准.自动驾驶自动驾驶的自动驾驶.摄像机 LiDAR 校准 校准数据融合数据融合没有目标的注册登记.

更多相关视频

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

16.7K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

527

相关实验视频

Last Updated: Jul 11, 2025

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

10.8K
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

16.7K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

527

科学领域:

  • 机器人和计算机视觉 机器人和计算机视觉
  • 传感器的融合和校准.

背景情况:

  • 精确的摄像头-激光雷达校准对于在自动驾驶等应用中有效的数据融合至关重要.
  • 数据驱动的校准方法提供了适应性,但与低于最佳的初始化参数作斗争.
  • 现有方法对初始化问题的敏感性阻碍了校准准确性和效率.

研究的目的:

  • 为摄像机-LiDAR校准提出一种新的一般模型,该模型解决了次优参数初始化问题.
  • 提高数据驱动校准技术的准确性和效率.
  • 开发一种可适应各种传感器配置的多功能校准方法.

主要方法:

  • 开发了一个通用的摄像机-LiDAR校准模型,抽象技术复杂性.
  • 引入了一个改进的目标函数,以减轻次优参数初始化.
  • 实现了多级参数优化算法,以实现平衡的准确性和效率.

主要成果:

  • 拟议的方法有效地减轻了低于最佳的初始校准参数的影响.
  • 取得了非常准确和高效的摄像机-LiDAR校准结果.
  • 在不同的传感器配置中展示了多功能性和适应性.

结论:

  • 这种新的校准方法为摄像机-LiDAR系统提供了显著的进步.
  • 该技术适用于各种应用,包括自动驾驶,机器人和计算机视觉.
  • 解决了当前数据驱动校准方法的关键局限性.