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Topographic Surveying and Contours01:29

Topographic Surveying and Contours

143
Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
143
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

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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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Updated: Jul 24, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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强大的扫描登记用于在森林环境中使用低分辨率LiDAR传感器进行导航.

Himanshu Gupta1, Henrik Andreasson1, Achim J Lilienthal1,2

  • 1Centre for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, Sweden.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,用于在林业中使用低分辨率LiDAR进行强大的同时定位和绘图 (SLAM) 和树木绘图. 该方法提高了导航和树木定位的准确性,无需GPS或IMU,解决了自动化森林机器的劳动力短缺问题.

关键词:
激光雷达 (LiDAR) 绘制地图斯拉姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯进行森林库存.林业机器人 林业机器人扫描 登记 扫描 登记树木细分 树木细分

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科学领域:

  • 机器人和自动化机器人与自动化
  • 林业技术 林业技术 林业技术
  • 计算机视觉 计算机视觉

背景情况:

  • 由于危险的条件和劳动力短缺,自动化林业机器至关重要.
  • 现有的SLAM方法在复杂的林业环境中扎,传感器数据有限.

研究的目的:

  • 开发一个强大的SLAM和树木绘制方法用于林业使用低分辨率LiDAR.
  • 为了提高导航准确度和树位定位,而不依赖GPS或IMU.

主要方法:

  • 使用树检测进行扫描登记和用低分辨率LiDAR (16Ch, 32Ch) 和固态LiDAR进行姿势校正.
  • 实现了自适应式预处理和启发式树检测.
  • 在私人和公共数据集上进行评估.

主要成果:

  • 实现了强大的扫描注册,优于通用算法,16Ch LiDAR的RMSE减少>3m,固态LiDAR的RMSE减少3.7m.
  • 与固定半径搜索相比,树木检测率增加了13%.
  • 自动树干直径估计使用4.3厘米MAE (6.5厘米RMSE).

结论:

  • 拟议的方法在林业条件下提供了强大的SLAM和树木映射,仅使用低分辨率LiDAR.
  • 这种方法可以提高林业机器的自动化,提高效率和安全性.