This paper introduces a bucket fill factor estimation method for earthmoving machinery aimed at solving sensor field-of-view blindness in measurements. Utilizing a point cloud repair technique, the method accurately reconstructs the 3D morphology of materials inside the bucket, even under occlusion conditions. The process begins by merging multiple frames of point cloud data to enhance information density. The material is then segmented from the comprehensive point cloud containing the bucket and other information. A repair strategy based on implicit surfaces reorganizes and fills holes in the point cloud. The Alpha Shape algorithm calculates the volume by using the filled point cloud. Extensive testing on loaders of different sizes proves the method’s robustness and shows significant accuracy improvements with the proposed data correction formula: 96.04% for small loaders and 95.36% for large loaders. Compared with existing volume estimation techniques, this method offers superior adaptability and reliability in real construction scenarios.

中文翻译：

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使用点云孔修复估算装载机的铲斗填充系数


本文介绍了一种用于土方机械的铲斗填充因子估计方法，旨在解决测量中传感器视场盲区的问题。该方法利用点云修复技术，即使在遮挡条件下也能准确重建桶内材料的 3D 形态。该过程首先合并多个点云数据帧以提高信息密度。然后从包含桶和其他信息的综合点云中分割材料。基于隐式曲面的修复策略会重新组织并填充点云中的孔。Alpha 形状算法使用填充的点云计算体积。对不同尺寸的装载机进行的广泛测试证明了该方法的稳健性，并表明所提出的数据校正公式的准确率显著提高：小型装载机为 96.04%，大型装载机为 95.36%。与现有的体积估算技术相比，该方法在实际施工场景中具有优异的适应性和可靠性。