Stockpile volumes are monitored by their companies as the product (i.e., aggregate, soil) is moved in and out of the facilities to ensure minimal product loss. Companies are mandated to report product movement to the government to ensure that the aggregate and soil is going where it is supposed to go. Many tools are used to monitor stockpile volumes including truck scales (to weigh incoming and outgoing trucks), light detection and ranging (LiDAR), Global Navigation Satellite System (GNSS) equipment, and unmanned aerial vehicle (UAV) photogrammetry. These processes give a good estimate of stockpile volumes. Errors in these estimates typically come from transportation and natural degradation of the stockpile. Not much research has been done on the best practices when using UAV photogrammetry to find the volume of a stockpile. Most recent research is about specific situations for finding a stockpile volume and whether UAV photogrammetry is as good as traditional methods for finding stockpile's volume. This study focuses on the effect of the flight height, camera angle, and presence of ground control points (GCP) in processing on the final volume calculated. Six UAV flights were done for this study; three different flight heights and two different camera angles. Additionally, the UAV reconstructed models were run with and without the GCPs to give twelve reconstructed volumes to examine for statistically significant differences. A similar study was done by Tucci et. al\cite{Tucci2019} where they focused on only camera orientation and found that the camera orientation was not statistically significant. We found that the differences between if GCPs in processing or not and between each flight elevation was statistically insignificant. We found that the differences in camera orientation between nadir and oblique were statistically significant. These different results could be due to many variables including differences in the dataset, differences in the statistical analysis, or the difference in stockpile size. We recommend using a high flight elevation and oblique photos to develop an efficient, accurate model.

<p dir="ltr">Several industrial and commercial bulk material management applications rely on accurate, current stockpile volume estimation. Proximal imaging and LiDAR sensing modalities can be used to derive stockpile volume estimates in outdoor and indoor storage facilities. Among available imaging and LiDAR sensing modalities, the latter is more advantageous for indoor storage facilities due to its ability to capture scans under poor lighting conditions. Evaluating volumes from such sensing modalities requires the pose (i.e., position and orientation) parameters of the used sensors relative to a common reference frame. For outdoor facilities, a Global Navigation Satellite System (GNSS) combined with an Inertial Navigation System (INS) can be used to derive the sensors’ pose relative to a global reference frame. For indoor facilities, GNSS signal outages will not allow for such capability. Prior research has developed strategies for establishing the sensor position and orientation for stockpile volume estimation while relying on multi-beam spinning LiDAR units. These approaches are feasible due to the large range and Field of View (FOV) of such systems that can capture the internal surfaces of barn and dome storage facilities.</p><p dir="ltr">The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, dust, and corrosive environment in deicing salt storage facilities) limit the use of such systems. With the increasing availability of solid-state LiDAR units, there is an interest in exploring their potential for stockpile volume estimation. In spite of their higher robustness to harsh conditions, solid-state LiDAR units have shorter distance measurement range and limited FOV when compared with multi-beam spinning LiDAR. This research presents a strategy for the extrinsic calibration (i.e., estimating the relative pose parameters) of installed solid-state LiDAR units inside stockpile storage facilities. The extrinsic calibration is made possible using deployed spherical targets and a complete, reference scan of the facility from another LiDAR sensing modality. The proposed research introduces strategies for: 1) automated extraction of the spherical targets; 2) automated matching of these targets in the solid-state LiDAR and reference scans using invariant relationships among them; and 3) coarse-to-fine estimation of the calibration parameters. Experimental results in several facilities have shown the feasibility of using the proposed methodology to conduct the extrinsic calibration and volume evaluation with an error percentage less than 3.5% even with occlusion percentages reaching up to 50%.</p>