Dynamic modelling of 3D stockpile for life-cycle management through sparse range point clouds

• We measure profiles of the stockpile using a 3DOF laser scanning device.
• We combine image processing and mathematical regression techniques for modelling.
• Improve the modelling accuracy by interpolating points from BWR cutting trajectory.
• Models can be applied directly to management systems for quality control.

Geometric stockpile models are always used to estimate the quantity and quality of iron ore in stockpile management systems. However, most of these models are not suitable for quality control purposes in iron-ore export facilities because no measurement device is used to continuously provide updated information for the modelling. This paper presents a solution to accurately measure profiles along the stockpile surface in real time and automatically models the 3D stockpile during stacking and reclaiming operations. Using a 2D laser range finder, the spatial data of a stockpile, such as the positions of different layers, the cutting surfaces during the reclaiming are represented by mathematical equations. Experiments conducted in a laboratory environment indicated good results and proved that the modelling approach is accurate and efficient. By making available the latest shapes of stockpiles and quality information, it is possible to predict the tonnage and quality grade with a greater degree of accuracy. Also, together with the reclaimer position data, it is possible to develop adaptive reclaiming algorithms to continually adjust the quality with respect to the quality requirement, which warrants meeting the required quantity and quality while enhancing the export efficiency and increasing productivity.