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Distance Corrections01:15

Distance Corrections

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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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Updated: May 28, 2025

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Camera-LiDAR Wide Range Calibration in Traffic Surveillance Systems.

Byung-Jin Jang1, Taek-Lim Kim2, Tae-Hyoung Park1

  • 1Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju 28644, Republic of Korea.

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Summary

This study introduces a new target-less camera-LiDAR calibration method using moving vehicles for traffic surveillance. It enhances accuracy in wide-area scenarios by constraining the search space and avoiding local optima.

Keywords:
calibrationgenetic algorithminfrastructureoptimizationsensors

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Accurate camera-LiDAR calibration is essential for traffic surveillance systems.
  • Large surveillance areas increase calibration complexity due to expanded sensor distances and search spaces.
  • Existing methods may struggle with the complexity of wide-area calibration.

Purpose of the Study:

  • To propose a novel target-less camera-LiDAR calibration method for traffic surveillance.
  • To address the challenges of large search spaces in wide-area calibration.
  • To enhance calibration accuracy and robustness in complex environments.

Main Methods:

  • Leveraging dynamic objects (moving vehicles) for calibration.
  • Employing a genetic algorithm-based optimization technique to constrain the search range.
  • Developing a target-less approach to simplify the calibration process.

Main Results:

  • Achieved high calibration accuracy in experimental results.
  • Demonstrated suitability for wide-area traffic surveillance applications.
  • Successfully constrained the calibration search range using dynamic objects.

Conclusions:

  • The proposed target-less method effectively calibrates camera-LiDAR systems for traffic surveillance.
  • Genetic algorithm optimization mitigates risks of local optima convergence.
  • This approach offers a promising solution for enhancing sensor fusion in complex surveillance settings.