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Related Concept Videos

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Linear Approximation in Time Domain

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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 frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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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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Updated: Jan 1, 2026

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Analysis and Improvements in AprilTag Based State Estimation.

Syed Muhammad Abbas1, Salman Aslam1, Karsten Berns2

  • 1Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan.

Sensors (Basel, Switzerland)
|December 18, 2019
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Summary
This summary is machine-generated.

AprilTag visual fiducial markers show reduced precision due to camera yaw angle. Proposed improvements enhance accuracy for precision localization tasks.

Keywords:
localizationrobot sensing and perceptionsensor modelling

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Visual fiducial markers like AprilTag are crucial for localization.
  • Understanding error sources in AprilTag systems is vital for precision applications.
  • Existing AprilTag libraries may lack the precision required for demanding tasks.

Purpose of the Study:

  • To analyze the accuracy and precision of AprilTag under various conditions.
  • To identify the primary sources of error affecting AprilTag performance.
  • To propose and validate methods for enhancing AprilTag's precision and accuracy.

Main Methods:

  • Error propagation analysis along horizontal axes and effect of yaw angle.
  • Development of trigonometric correction for yaw angle.
  • Implementation of a custom yaw-axis gimbal for real-time tracking.
  • Creation of a pose-indexed probabilistic sensor error model using Gaussian Processes and particle filter validation.

Main Results:

  • Camera yaw angle identified as the primary error source, significantly impacting precision.
  • Distance and viewing angle also affect AprilTag accuracy.
  • Proposed improvements, including yaw correction and a probabilistic error model, significantly enhance system accuracy and precision.

Conclusions:

  • AprilTag's precision is highly sensitive to camera yaw angle.
  • The proposed improvements substantially increase AprilTag's accuracy and precision.
  • Enhanced AprilTag systems are suitable for high-precision indoor and outdoor localization applications.