04/30/2026
The Inventory Challenge: Turning Volumetric LiDAR Scans into Accurate Weight Estimation
Body: Inventory measurement for highly compressive bulk materials—like the shredded paper seen in our first image—is a notorious industrial puzzle. Volume measurement alone isn’t enough. Our visual comparison shows how we use LiDAR to generate a precise point-cloud topology, creating a complete visual twin of the inventory within the bunker. Using a local CPU to process this data, we fuse raw point clouds into a realtime volumetric model.
The Volumetric-to-Weight Problem
Calculating volume is only step one. Multiplying that volume by a simple average density leads to massive, costly errors. Why? Compaction.
As visualized in the concept test image of the pellets on the scale, the density of a deep material pile is not uniform. The weight of the material crushes itself at the bottom of the stack, making it much denser than the loose material on the surface. Effective density increases significantly with pile height.
Our Solution: Calibration and Compensation
We don’t rely on generic density factors. To achieve accurate weight estimation, we empirically characterize the material to understand how it compacts over different heights. This empirical data is then used in a compaction compensation algorithm processed right at the edge by the CPU.
This work also revealed another crucial detail: storage bunkers are rarely identical. Even slight structural differences will skew results. Our system now requires an individual “empty state” baseline scan for each unique storage bay to ensure our reference plane is perfect for each specific location. By combining LiDAR-driven volume measurement with empirical compaction analysis, we provide precise inventory data, preventing expensive stockouts and optimizing logistics.
How is your team using sensor fusion and material characterization to improve inventory data? Let’s discuss your challenges in the comments.
