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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Instrument Calibration01:12

Instrument Calibration

644
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
644
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

854
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.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
854
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

675
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
675
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

4.5K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
4.5K
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

779
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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Related Experiment Video

Updated: Jan 7, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

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Targetless Radar-Camera Calibration via Trajectory Alignment.

Ozan Durmaz1, Hakan Cevikalp1

  • 1Electrical and Electronics Engineering Department, Eskisehir Osmangazi University, Meselik, Eskisehir 26040, Turkey.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a targetless method for calibrating radar and camera sensors, crucial for autonomous navigation. It achieves accurate sensor alignment by tracking object trajectories, enabling reliable multi-modal perception without physical markers.

Keywords:
cameraradarsensor calibrationsynchronizationtrajectory alignment

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Accurate extrinsic calibration between radar and camera sensors is vital for multi-modal perception in robotics and autonomous navigation.
  • Traditional calibration methods using artificial targets are often impractical in dynamic or large-scale environments.

Purpose of the Study:

  • To present a fully targetless calibration framework for estimating the rigid spatial transformation between radar and camera coordinate frames.
  • To enable practical, markerless multi-sensor calibration for real-world autonomous systems.

Main Methods:

  • Integrating YOLOv5-based 3D object localization with DBSCAN and RANSAC for radar data processing.
  • Employing a passive temporal synchronization technique using RMSE minimization to correct timestamp offsets.
  • Utilizing Kabsch and Umeyama algorithms for computing rigid transformation parameters.

Main Results:

  • Achieved sub-degree rotational accuracy and decimeter-level translational error (0.12-0.27 m).
  • Demonstrated robust alignment despite radar sparsity and measurement bias.
  • Validated generalization to unseen motion trajectories using a drone as a dynamic target.

Conclusions:

  • The proposed targetless framework offers a practical solution for radar-camera calibration.
  • The method is suitable for real-world autonomous systems requiring markerless multi-sensor calibration.
  • The findings contribute to advancing reliable multi-modal perception in robotics.