Measuring Vehicle Profile Size: Lidar-Based System and K-Frame-Based Methodology

Area of Science:

• Engineering
• Computer Vision
• Metrology

Background:

• Traditional light curtain systems for vehicle profile measurement are susceptible to environmental factors like temperature and humidity.
• Existing methods lack robustness and accuracy in diverse weather conditions.

Purpose of the Study:

• To propose and validate a novel lidar-based system employing a K-frame algorithm for precise vehicle profile size measurement.
• To address the limitations of current vehicle profiling technologies by offering a weather-resilient solution.

Main Methods:

• Development of a lidar-based system comprising multiple lidar sensors and an industrial computer.
• Investigation and optimization of a K-frame-based algorithm through experimental validation.
• Comparative analysis against vision-based and other lidar-based vehicle profiling methods.

Main Results:

• An optimal algorithm combination, A16, was identified through systematic experimentation.
• The proposed system and K-frame methodology exhibited excellent generality and repeatability across various vehicle types.
• The lidar-based K-frame approach significantly outperformed comparative vision-based and lidar-based methods in accuracy.

Conclusions:

• The proposed lidar-based system with the K-frame algorithm offers a highly accurate and reliable solution for vehicle profile measurement.
• This novel methodology overcomes the environmental sensitivities associated with light curtain systems.
• The K-frame algorithm provides a robust foundation for advanced vehicle metrology applications.