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

相关实验视频

Updated: Jun 21, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.1K

地下采矿轴传感器数据收集方法

Artur Adamek1, Janusz Będkowski2, Paweł Kamiński3

  • 1Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括

相关概念视频

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

An Open and Novel Low-Cost Terrestrial Laser Scanner Prototype for Forest Monitoring.

Sensors (Basel, Switzerland)·2026
Same author

Affordable Robotic Mobile Mapping System Based on Lidar with Additional Rotating Planar Reflector.

Sensors (Basel, Switzerland)·2023
Same author

Evaluation of Technical Condition and Durability of Wooden Shaft Guides with Application of Non-Destructive and Semi-Destructive Testing Methods.

Materials (Basel, Switzerland)·2022
Same author

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.

Materials (Basel, Switzerland)·2022
Same author

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence.

Materials (Basel, Switzerland)·2022
Same author

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning.

Materials (Basel, Switzerland)·2022

研究人员创建了第一个开放访问的地下矿井数据集. 这种有价值的资源支持各种应用,包括采矿环境中的3D测量和人工智能.

科学领域:

  • 地质科学 地质科学
  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉

背景情况:

  • 现有的文献缺乏对地下采矿井的开放访问数据集.
  • 这种数据缺口限制了诸如轴检查和自主导航等领域的研究.
  • 地下采矿环境为数据采集和分析带来了独特的挑战.

研究的目的:

  • 为了解决开放访问地下矿井数据的稀缺性.
  • 为推进移动绘图和人工智能研究提供全面的数据集.
  • 在采矿中建立3D测量和同时定位和映射 (SLAM) 的基准.

主要方法:

  • 使用多个LiDAR传感器 (Velodyne VLP-16,Velodyne Ultra Puck VLP-32c,Livox Tele-15) 和惯性测量单元 (IMU Xsens MTi-30) 的数据采集.
  • 通过15个地面控制点的地质调查收集高精度地面真相数据.
  • 使用6个Faro Focus 3D站进行地面激光扫描,捕获超过2.73亿个3D点.

主要成果:

  • 开发首个专门针对地下采矿井 (深度为-300米) 的开放访问数据集.
  • 一个丰富的数据集,包括数百万个适合各种研究应用的3D点.
关键词:
在IMU,IMU是IMU.李达尔 (LiDAR) 是一种激光雷达.矿山地图绘制 矿山地图绘制地下坑道测绘 地下坑道测绘

更多相关视频

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.2K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.2K

相关实验视频

Last Updated: Jun 21, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.1K
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.2K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.2K
  • 在采矿环境中展示移动绘图技术的现实应用.

    • 结论:

    • 该数据集成功地填补了地下采矿可用的研究资源中的关键缺口.
    • 数据的开放性质将促进人工智能,机器人技术和采矿勘测方面的创新.
    • 这项工作为未来关于地下基础设施监测和管理的研究提供了基础.