  Non-contact volume estimation of pile-type objects has considerable potential
in industrial scenarios, including grain, coal, mining, and stone materials.
However, using existing method for these scenarios is challenged by unstable
measurement poses, significant light interference, the difficulty of training
data collection, and the computational burden brought by large piles. To
address the above issues, we propose the Depth Integrated Volume EStimation of
Pile Of Things (DIVESPOT) based on point cloud technology in this study. For
the challenges of unstable measurement poses, the point cloud pose correction
and filtering algorithm is designed based on the Random Sample Consensus
(RANSAC) and the Hierarchical Density-Based Spatial Clustering of Applications
with Noise (HDBSCAN). To cope with light interference and to avoid the relying
on training data, the height-distribution-based ground feature extraction
algorithm is proposed to achieve RGB-independent. To reduce the computational
burden, the storage space optimizing strategy is developed, such that accurate
estimation can be acquired by using compressed voxels. Experimental results
demonstrate that the DIVESPOT method enables non-data-driven, RGB-independent
segmentation of pile point clouds, maintaining a volume calculation relative
error within 2%. Even with 90% compression of the voxel mesh, the average error
of the results can be under 3%.
