This analysis shows stable measurement error of ±5 cm in automated external dimension detection of engineering vehicles, suggesting enhanced industrial applications.
Aiming at the technical challenges of large dust interference, complex measurement parameters, and high real-time requirements in the automated sampling scenario of iron powder transportation vehicles, a method for external dimension detection that integrates laser radar and multi-algorithm collaboration is proposed. By improving ICP point cloud registration, Moving Least Squares surface reconstruction (MLS+), and Gaussian mixture model (GMM-EM) algorithms, the full process automation measurement of carriage length/width/height, top angle coordinates, and reinforcement positions is achieved. Experiments have shown that the system maintains a stable measurement error within ±5 cm and a single-frame processing time of ≤2.1 s in environments with PM2.5 ≤ 500 μg/m3, providing an innovative solution for intelligent detection in industrial scenarios.
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Wu et al. (2025) studied this question.
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