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在LIDAR点云中的尘埃过使用深度学习用于采矿应用程序.

Bruno Cavieres1, Nicolás Cruz2, Javier Ruiz-Del-Solar1,2

  • 1Department Electrical Engineering, Universidad de Chile, Santiago 837-0451, Chile.

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概括
此摘要是机器生成的。

一种新的神经网络方法可以为采矿操作实时过LIDAR数据中的尘埃. 这项研究引入了一个公共数据库,用于培训和比较尘埃过技术.

关键词:
在LIDAR中禁止噪音.这是一个点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网.深度学习是一种深度学习.过尘埃过器的使用方法

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科学领域:

  • 地理空间技术是什么?
  • 采矿中的人工智能

背景情况:

  • 激光雷达传感器提供精确的3D环境数据,对于采矿至关重要.
  • 矿山灰尘严重阻碍了LIDAR传感器的功能,影响了数据的准确性.

研究的目的:

  • 为采矿环境中的LIDAR点云开发实时尘埃过方法.
  • 创建一个公共数据库,以推进尘埃过研究.

主要方法:

  • 一种基于神经网络的方法被设计用于实时测尘过.
  • 该方法经过训练和验证,使用来自灰尘矿场的现实世界激光雷达数据.
  • 建立了一个全面的公共数据库,包含来自各种灰尘环境的LIDAR数据.

主要成果:

  • 拟议的神经网络方法有效地在实时中过LIDAR点云中的尘埃.
  • 该方法在采矿应用中的尘埃过中实现了最先进的性能.
  • 公共数据库为研究界提供了宝贵的资源.

结论:

  • 开发的神经网络方法提供了一个强大的解决方案,用于克服矿业LIDAR传感中的尘埃干扰.
  • 公共数据库将加速未来尘埃过算法的开发和基准测试.