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Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
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Common Leveling Mistakes and Errors01:17

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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...
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Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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Related Experiment Video

Updated: Jul 29, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
09:04

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Machine learning-aided LiDAR range estimation.

Daniel Bastos, Bruno Faria, Paulo P Monteiro

    Optics Letters
    |May 24, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces decision tree ensemble machine learning to enhance automotive light detection and ranging (LiDAR) systems. The new method achieves accurate range measurements across a wide dynamic range without sacrificing efficiency.

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    Area of Science:

    • Optics and Photonics
    • Machine Learning Applications
    • Automotive Sensing Technology

    Background:

    • Automotive light detection and ranging (LiDAR) systems need efficient range estimation.
    • Current methods achieve efficiency by limiting the receiver's dynamic range, impacting performance.
    • A trade-off exists between efficiency and dynamic range in LiDAR receivers.

    Purpose of the Study:

    • To propose a novel approach for accurate and computationally efficient range estimation in automotive LiDAR.
    • To overcome the dynamic range limitations of current LiDAR receiver technologies.
    • To leverage machine learning for improved LiDAR performance.

    Main Methods:

    • Development of simple yet powerful decision tree ensemble machine learning models.
    • Application of these models to automotive LiDAR range estimation.
    • Testing and validation of model performance across a significant dynamic range.

    Main Results:

    • Accurate range measurements were achieved using the proposed machine learning models.
    • The models successfully operated across a 45-dB dynamic range.
    • The decision tree ensemble approach effectively addressed the efficiency-dynamic range trade-off.

    Conclusions:

    • Decision tree ensemble models offer a viable solution for enhancing automotive LiDAR systems.
    • This machine learning approach enables accurate and efficient range estimation over extended dynamic ranges.
    • The findings pave the way for more robust and capable LiDAR technology in autonomous vehicles.