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A stroke engine has a slider-crank mechanism that 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.
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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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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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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Related Experiment Video

Updated: May 5, 2026

High-speed Particle Image Velocimetry Near Surfaces
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Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State.

Mena Nagiub1,2,3, Thorsten Beuth4, Ganesh Sistu2,3,5

  • 1Department of Front Camera, Valeo Schalter und Sensoren GmbH, 74321 Bietigheim-Bissingen, Germany.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stage method to improve depth map accuracy for indirect Time-of-Flight (iToF) Lidar sensors in dynamic outdoor environments by addressing motion blur.

Keywords:
Lidarambiguityblind deconvolutionblurrinesscontinuous learningdepth correctionestimationiTOFmotion blurnear field

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Technology

Background:

  • Current deep learning methods for iToF Lidar phase unwrapping are limited to static indoor scenes.
  • Motion blur in dynamic outdoor environments significantly degrades depth map quality.
  • Existing techniques fail to address the challenges posed by real-world dynamic scenarios.

Purpose of the Study:

  • To develop a robust phase unwrapping technique for iToF Lidar sensors in dynamic outdoor scenes.
  • To effectively mitigate motion blur artifacts in depth maps.
  • To enhance the accuracy and reliability of depth sensing for autonomous systems.

Main Methods:

  • A two-stage semi-supervised learning approach is proposed.
  • Initial training on static datasets for depth map prediction.
  • Adaptation to dynamic datasets using continuous learning and blind deconvolution to reduce motion blur.

Main Results:

  • The proposed method successfully unwraps ambiguous depth maps affected by motion blur.
  • Significant reduction in blur noise and improved depth map quality were achieved.
  • Demonstrated effectiveness in dynamic outdoor scenarios, outperforming existing methods.

Conclusions:

  • The developed technique offers a viable solution for accurate depth sensing in challenging dynamic environments.
  • This advancement is crucial for improving the performance of autonomous vehicles and robots.
  • The method provides high-quality depth maps essential for real-world applications.