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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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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...
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Related Experiment Video

Updated: Dec 27, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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Distance Error Correction in Time-of-Flight Cameras Using Asynchronous Integration Time.

Eu-Tteum Baek1, Hyung-Jeong Yang1, Soo-Hyung Kim1

  • 1Department of Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Gwangju 61186, Korea.

Sensors (Basel, Switzerland)
|February 26, 2020
PubMed
Summary

This study introduces a novel method to correct depth errors in time-of-flight (ToF) sensors, improving distance map accuracy for shiny and dark surfaces. The technique utilizes multiple ToF sensors with varied integration times to refine depth information and reduce noise.

Keywords:
3D warpingasynchronous integration timeoptical noise reduction filtertime-of-flight

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Technology

Background:

  • Time-of-flight (ToF) depth sensors face challenges with depth ambiguity on shiny/dark surfaces and optical noise.
  • Inherent hardware limitations in ToF sensors hinder accurate distance measurements due to light reflection and absorption.
  • Existing methods struggle to overcome these fundamental drawbacks of ToF technology.

Purpose of the Study:

  • To develop a robust distance error correction method for ToF depth sensors.
  • To enhance the accuracy and reliability of depth maps, particularly in challenging surface conditions.
  • To mitigate issues like ambiguous depth information, optical noise, and boundary mismatches.

Main Methods:

  • Employed three ToF depth sensors configured with different integration times to capture multiple distance maps.
  • Estimated error regions and amplitude maps based on light intensity.
  • Refined depth information by leveraging data from neighboring sensors with varying integration times.
  • Introduced a novel optical noise reduction filter considering depth information distribution.

Main Results:

  • Successfully addressed depth ambiguities in shiny and dark surfaces.
  • Significantly reduced depth errors caused by excess reflection and absorption.
  • Demonstrated effective reduction of optical noise and improved boundary matching.
  • Validated the enhanced accuracy and reliability of the generated distance maps.

Conclusions:

  • The proposed method effectively overcomes the inherent limitations of ToF cameras.
  • The multi-sensor integration time approach provides significantly enhanced distance map accuracy.
  • This technique offers a practical solution for improving depth sensing in real-world applications.