Estimating bucket fill factor for loaders using point cloud hole repairing

Abstract

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.