Specialized LiDAR Scanning Strategies for Stockpile Volumetric Measurement

Estimating stockpile volume accurately is important for managing industrial inventory. Traditional methods, like surveying, are slow, require a lot of work, and are often unreliable in tough industrial conditions such as dust or low light. This study introduces an automated LiDAR-based solution that uses the Velodyne VLP-16 sensor for 3D mapping in any weather. The method consists of seven steps: extracting raw PCAP, transforming coordinates, registering multi-level ICP (Iterative Closest Point) with motion prediction, segmenting the ground using RANSAC (Random Sample Consensus), clustering with DBSCAN (Density-Based Spatial Clustering of Applications with Noise), normalizing the ground, and calculating volume with Delaunay triangulation. Testing on a controlled sandpile shows high accuracy, with a 2.1% error rate. This method cuts down time by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 5 \%}$</tex> and labor by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 0 \%}$</tex> compared to traditional methods. Future work will focus on deploying this technology in real-time at INSEE Cement Sri Lanka for monitoring in Industry 4.0.