Stockpile Volume Research Articles

Automated extrinsic calibration of solid-state frame LiDAR sensors with non-overlapping field of view for monitoring indoor stockpile storage facilities

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 indoor storage facilities.The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, wide range of temperature variation, 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. Despite 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%.

Perhitungan Volume Stockpile dan Pengoptimalan Waktu Pengolahan Stockpile 3, 5 dan 6 Terhadap Produksi Alat Gali muat dan Alat Angkut pada TB Primer Batubesi PT Timah Tbk

PT Timah Tbk's Batubesi Primary Large Mine has experienced a significant decline in production in recent years, causing a change in the mining process to stockpile management that is still economical with a grade of 0.2%. This study aims to determine the volume and production of stockpiles 3, 5 and 6, the type of sediment and Sn content of stockpile material and the time required to spend stockpile material 3, 5 and 6 in order to get the optimal time. The method used is a quantitative method by taking cycle time data as much as 30 data and analyzing sample levels using XRF on each stockpile. The results of this study obtained the volume of stockpile 3 material is 132,648.42 m3,, stockpile 5 is 162,570.25 m3, and stockpile 6 is 33,090.19 m3. The calculated machine productivity at stockpile 3 is 42.17 tons/hour, stockpile 5 is 48.11 tons/hour and stockpile 6 is 39.53 tons/hour. The highest Sn content is found in stockpile 6 which is 0.33% with the type of skarn sediment. While the lowest Sn content is found in stockpile 3 which is 0.2% with oxide clay sediment type. In stockpile 5, the Sn content is 0.31% with the type of oxide clay sediment. The remaining volume of stockpile material in stockpiles 3, 5 and 6 can be spent with a time of 2.78 years using 1 unit of excavation equipment and 1 unit of transportation equipment. Optimization is carried out so that the remaining stockpile reserves can be spent within 1.74 years using 1 unit of excavation equipment and 2 units of transport equipment.

ALTERNATIVE LIDAR TECHNOLOGIES FOR STOCKPILE MONITORING AND REPORTING

Abstract. Accurate volume estimation of salt stockpiles stored in covered facilities is essential for effective management and budgeting in the transportation industry. Due to environmental concerns, salt is stored in indoor facilities. The surveying tools that are widely applied for outdoor stockpile estimation such as Global Navigation Satellite System (GNSS) Receivers and Uncrewed Aerial Vehicles (UAV) are not applicable for indoor mapping. To address this limitation, our prior research proposed and developed a Stockpile Monitoring and Reporting Technology (SMART) which was designed for indoor stockpile volume estimation. This study builds upon that prior research to evaluate the feasibility and performance of different LiDAR alternatives within the SMART system. Three LiDAR sensors (Velodyne VLP-16, Ouster OS1-32-U, and Blickfeld Cube 1) are compared in terms of system calibration, point cloud registration, and volume estimation. Results demonstrate the impact of LiDAR sensor choice on system performance, occlusion rates, and volumetric accuracy. The findings contribute to expanding the versatility and adaptability of LiDAR technology in SMART applications, allowing for more efficient and accurate stockpile volume estimation.

Assessment of Difference in Structure from Motion for Stockpile Volume Estimation using UAV Approach

The software for processing the photogrammetry software is increasingly capable of producing a high-quality 3D model DEM, DSM, and DTM from an aerial photo and measuring the volume of material. Nowadays, UAV technology has been rapidly used for stockpile volume calculation for the mining and construction industry. The purpose of this study is to compare image processing for the stockpile volume calculation using three different software for the UAV with the conventional method GPS as a reference. The result obtained using GPS is processed using CIVIL3D and act as a base measurement of the stockpile volume to compare result obtain from photogrammetry software which is Pix4D, Agisoft, and SimActive’s Correlator3D. The analysis of this study compared 3D model for volume calculation from a photo image for each photogrammetry software selected and obtained the result volume calculation for photogrammetry with the accuracy achieving below 3% error from the referenced volume of the stockpile. 12 GCP were placed around the study area to test the difference between the numbers of GCP processing for the volume stockpile results. The image acquisition is taken at 10 meter altitude and 20 meter altitude using UAV Phantom 4 pro and set with Pix4Dcapture circular mission. The processing method is SfM in the 3 photogrammetric selected that have 4 types of processing for each software, 10 meter altitude and 20 meter altitude with 4 GCP and 12 GCP. 12 results of the 3D model and volume of the stockpile was obtained. As for the volume results, the Pix4D 10 meter 4 GCP result obtained the closest volume to the reference. The point cloud generated is also stable based on the cloud compare volume density result. Overall, the study can conclude that not only the altitude of flight affected, and the type of software used is affecting the number of point clouds generated but also the number of GCP.

JUSTIFICATION OF THE EVALUATION INDICATORS AND THE MECHANISM FOR IMPROVING THE EFFICIENCY OF THE MANAGEMENT SYSTEM OF THE MATERIAL RESOURCES SUPPLY OF THE OPERATIONAL TACTICAL GROUP OF TROOPS DURING THE CONDUCT OF A DEFENSE OPERATION

In modern conditions, the general defense system should provide the possibility of centralized and decentralized management of material resources and be able to participate in the management of state defense. The effective functioning of the system of providing material resources is influenced by the timeliness and completeness of the financing of the needs of comprehensive provision of troops (forces). The reform of the Armed Forces of Ukraine envisages the introduction of electronic accounting systems for material and technical resources by NATO supply classes, bringing the volume of indestructible stockpiles of material resources in line with the real needs for these resources. In order to achieve strategic goals and fulfill the main tasks defined by the Strategic Defense Bulletin of Ukraine and the State Program for the Development of the Armed Forces of Ukraine, planned phased work is underway to implement reforms aimed at developing a single effective logistics system according to NATO standards, capable of providing effective support to all components of the defense forces , who, in the established order and under the conditions determined by the legislation of Ukraine, are involved in the implementation of the task of defense of the state as part of the defense forces. In addition, the experience of the Armed Forces of Ukraine and other components of the defense forces in the conditions of armed aggression by the Russian Federation indicates the need to create a system of providing material and technical means, works and services as a component of the system of providing the Armed Forces of Ukraine and other components of the defense forces during the joint performance of tasks with defense of the state. After all, the organization of the material support of the Armed Forces of Ukraine in practice showed that the existing system of rear, technical and medical support of the Armed Forces of Ukraine turned out to be limited in its ability to perform tasks during mobilization and operational deployment, as well as directly in the interests of the use of groups of troops (forces), therefore its functioning, both in peacetime and in a special period, needs drastic changes.

Remarks on Geomatics Measurement Methods Focused on Forestry Inventory.

This contribution focuses on a comparison of modern geomatics technologies for the derivation of growth parameters in forest management. The present text summarizes the results of our measurements over the last five years. As a case project, a mountain spruce forest with planned forest logging was selected. In this locality, terrestrial laser scanning (TLS) and terrestrial and drone close-range photogrammetry were experimentally used, as was the use of PLS mobile technology (personal laser scanning) and ALS (aerial laser scanning). Results from the data joining, usability, and economics of all technologies for forest management and ecology were discussed. ALS is expensive for small areas and the results were not suitable for a detailed parameter derivation. The RPAS (remotely piloted aircraft systems, known as "drones") method of data acquisition combines the benefits of close-range and aerial photogrammetry. If the approximate height and number of the trees are known, one can approximately calculate the extracted cubage of wood mass before forest logging. The use of conventional terrestrial close-range photogrammetry and TLS proved to be inappropriate and practically unusable in our case, and also in standard forestry practice after consultation with forestry workers. On the other hand, the use of PLS is very simple and allows you to quickly define ordered parameters and further calculate, for example, the cubic volume of wood stockpiles. The results from our research into forestry show that drones can be used to estimate quantities (wood cubature) and inspect the health status of spruce forests, However, PLS seems, nowadays, to be the best solution in forest management for deriving forest parameters. Our results are mainly oriented to practice and in no way diminish the general research in this area.

Stockpile Volume Estimation in Open and Confined Environments: A Review

This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces.

ANALYSIS OF POSSIBLE GEODETIC APPROACHES FOR 3D MODEL CREATION IN BIM

Abstract. The topic of this paper is the description and analysis of technologies for generating a geometric basis for BIM. The main element is an analysis of the capabilities of mobile laser scanning and the outputs of this method compared to other methods. The selected interior space was measured by different technologies, laser scanning, classical geodetic using total station, and manual measurement. The main device tested was the ZEB-REVO. It is a handheld mobile laser scanner, carried by an operator. It has a range of 40 m, with the accuracy in measured lengths is 1–3cm per 10 m depending on the type of surface. The scanning speed is 43,000 points per second. The device is equipped with an inertial measurement unit (IMU) and uses SLAM technology for trajectory definition. The device does not have a camera in the simplest configuration and the operator cannot monitor the scanning status. Nevertheless, measurement with this scanner is very simple. The instrument is mainly used for surveying construction objects, but it can be advantageously used for documentation of underground spaces or in e.g., forestry for defining DBH or volume of stockpiles. This device is not equipped with a GNSS unit, and the typical scanning time is 20–30 min without model deformation. Accuracy and output analysis was performed for all technologies used. The results are presented in tables and show the suitability of PLS (personal laser scanner) in general.

INTEGRATION ANALYSIS OF DRONE MULTI SENSOR-GNSS-LIDAR-CAMERA FOR 3D MAPPING (CASE STUDY: PT GARAM, PAMEKASAN, MADURA)

Every year salt was carried out by PT Garam twice whereas the previous process used the method of terrestrial and manual calculations with sacks. Therefore, a map of salt production is needed using the Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor navigation system to increase the data's accuracy level. Unmanned Aerial Vehicle applications for the purpose of obtaining geometric documentation results and for capturing textures that characterize object structures. (Adamopoulos, 2020). LiDAR can classify based on the number of image acquisitions processed in the software, utilizing photogrammetric, and structural science principles from Motion technology. (Fernández, Hernandez et.al 2015) LiDAR Acquisition process that enables the creation of point clouds, three-dimensional models, and Digital Surface Models with high accuracy. (Barba, et al. 2019). A three-dimensional model makes it easy for users to choose a virtual position on the map, has good accuracy in interpreting the map, and displays a more real shape. LiDAR has a disadvantage in the form of coordinate data positions that have local references. (Cahyadi., et al. 2019). Unmanned Aerial Vehicle (UAV) technology has the advantage of efficiency in the cost and time of data collection. These researchers use multi-sensor technology GNSS, LiDAR, and Drones to map the stockpile of salt on open land and in warehouses. makes it easy to calculate the area of salt land and calculate the volume of salt production. This was done for PT GARAM to meet the community's needs and monitor the amount of salt production. This data can be used as a reference for digital asset inventory and the system can be applied to optimize the salt industry. The drones used in this study are the DJI Matrice 300 and the Low-Cost Drone Tarot Iron Man 650. Both types of drones are used to measure land and salt volume. The end product of the research is in the form of data generated in the form of a point cloud to calculate the salt stockpile volume and the elevation level of the salt land area

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Construction materials and related management, handling, and storage provisions account for a large portion of road construction expenses. For that reason, improved material flow monitoring techniques can achieve significant cost and time savings, as well as better quality control. This study assessed the performance of a solid-state pulsed time-of-flight laser lidar profilometer in measuring the volume of soil stockpiles and road layer thicknesses. The 3D (X, Y, Z, and intensity) image calculation was based on the analysis of multiple combined point clouds measured with an excavator-integrated profilometer. Error analysis confirmed the accuracy of road layer thickness estimation within one centimeter and an error level of approximately 3% when measuring soil stockpile volumes. In conjunction with a theoretical model of the superstructure, this 3D measurement technique can help contractors and supervisors ensure road quality.

Stockpile Volume Estimation Calculation Based on Terrestrial Laser Scanner (TLS) Data Acquisition and 3D Surface Visualization

Stock taking is a series of activities to calculate the stock of goods that are still stored in the warehouse to be marketed. There are many activities covered in it, ranging from calculating the number of goods, conducting direct inspections, and structuring that will facilitate business operations when a certain product is needed. One of these activities is also carried out in the mining sector. Coal stock-taking is a survey activity carried out in the coal yard area to calculate the volume of the stockpile and coal tonnage after being multiplied by the density value. Large-dimensional coal stocking must be carried out quickly, accurately and in detail. The need for this can be obtained using laser scanner technology. Laser scanner is a tool designed to scan the surface of an object and represent it in 3D in the form of a height density point cloud. Based on this, in carrying out stock-taking calculation activities, measurements are needed which mainly aim to find out the stockpile volume and density in the fourth quarter of the Adipala PLTU Coal Yard. Stockpile measurement method using volumetric method. Measurement using a Laser Scanner tool to obtain the shape of the stockpile area is by seizing the entire surface of the Stockpile by setting the resolution of the density of coordinate points (x, y, z) as needed. Tool displacement when measurements are made on every detail of the Stockpile curve. Based on the calculation results, it is known that the volume value of the coal stockpile on the west side coal yard is 121,420,574 m3 and the east side coal yard is 88,230,355 m3 on. The total volume of coal amounted to 209,650,929 m3 then multiplied by the density of the bulk density survey results and obtained the tonnage of 180,384,417 MT.

An Image-Aided Sparse Point Cloud Registration Strategy for Managing Stockpiles in Dome Storage Facilities

Stockpile volume estimation plays a critical role in several industrial/commercial bulk material management applications. LiDAR systems are commonly used for this task. Thanks to Global Navigation Satellite System (GNSS) signal availability in outdoor environments, Uncrewed Aerial Vehicles (UAV) equipped with LiDAR are frequently adopted for the derivation of dense point clouds, which can be used for stockpile volume estimation. For indoor facilities, static LiDAR scanners are usually used for the acquisition of point clouds from multiple locations. Acquired point clouds are then registered to a common reference frame. Registration of such point clouds can be established through the deployment of registration targets, which is not practical for scalable implementation. For scans in facilities bounded by planar walls/roofs, features can be automatically extracted/matched and used for the registration process. However, monitoring stockpiles stored in dome facilities remains to be a challenging task. This study introduces an image-aided fine registration strategy of acquired sparse point clouds in dome facilities, where roof and roof stringers are extracted, matched, and modeled as quadratic surfaces and curves. These features are then used in a Least Squares Adjustment (LSA) procedure to derive well-aligned LiDAR point clouds. Planar features, if available, can also be used in the registration process. Registered point clouds can then be used for accurate volume estimation of stockpiles. The proposed approach is evaluated using datasets acquired by a recently developed camera-assisted LiDAR mapping platform—Stockpile Monitoring and Reporting Technology (SMART). Experimental results from three datasets indicate the capability of the proposed approach in producing well-aligned point clouds acquired inside dome facilities, with a feature fitting error in the 0.03–0.08 m range.

Indoor Stockpile Reconstruction Using Drone-Borne Actuated Single-Point LiDARs

A low-cost, yet accurate approach for stockpile volume estimation within confined storage spaces is presented. The novel approach relies on actuating a single-point light detecting and ranging (1D LiDAR) sensor using a micro servo motor onboard a drone. The collected LiDAR ranges are converted to a point cloud that allows the reconstruction of 3D stockpiles, hence calculating the volume under the reconstructed surface. The proposed approach was assessed via simulations of a wide range of mission operating conditions while mapping two different stockpile shapes within the Webots robotic simulator. The influences from modulating the drone flight trajectory, servo motion waveform, flight speed, and yawing speed on the mapping performance were all investigated. For simple rectangular trajectories, it was found that having longer trajectories that are adjacent to the storage walls provides best reconstruction results with reasonable energy consumption. On the other hand, for short rectangular trajectories within the storage middle space, the yawing speed at corners must be decreased to ensure good reconstruction quality, although this can lead to relatively high energy consumption. Comparing the volumetric error values, the average error from the proposed 1D LiDAR system, when operating at 6°·s−1 maximum yawing speed at the corners, was 0.8 ± 1.1%, as opposed to 1.8 ± 1.7%, and 0.9 ± 1.0% from the 2D and 3D LiDAR options, respectively. Moreover, compared to 2D and 3D LiDARs, the proposed system requires less scanning speed for data acquisition, is much lighter, and allows a substantial reduction in cost.

A Combined Field–Lab Approach for Assessing Salmonella Infantis Persistence in Broiler Litter in a Stockpile and Composting Sleeve

Broiler litter (BL) is often contaminated by a variety of zoonotic pathogens. This study attempts to assess the persistence of Salmonella enterica serovar Infantis (S. Infantis) in BL based on spatial and temporal variation of physicochemical properties in a stockpile and composting sleeve. A single trial of two pilot-scale setups, ~35 m3 each, included an open static pile (stockpile) and composting in a polyethylene sleeve with forced aeration. The initial water content was adjusted only for the sleeve (~50% w/w) as in a common composting practice. Both systems were monitored weekly and then biweekly during 2 months in 47–53 sampling points each on every campaign. Measurements included temperature, water content, pH, electrical conductivity (EC), gas-phase oxygen, and ammonia, and the collected data were used to construct multiple contour grid maps. Of the stockpile volume, 83, 71, and 62% did not reach the commonly required minimum temperature of 55°C for three consecutive days during the first, second, and third weeks, respectively. Oxygen levels showed a strong gradient across the stockpile, while anaerobic conditions prevailed in the core. Variation was also recorded within the sleeve, but due to the water content adjustment and active aeration, the conditions favored more intense degradation and higher temperatures. Combining the grid maps drawn in this study with decay rate constants recently published for S. Infantis in BL under 36 combinations of temperature, water content, and pH, we assessed the spatial persistence of S. Infantis in the stockpile and the sleeve. Temperature was shown as a major factor, while water content and pH had only a small effect, in the stockpile only. Co-correlations between temperature, water content, EC, and oxygen suggest that selected physicochemical properties may be sufficient for such assessments. Up to 3 weeks would be recommended to achieve 7–8 log10 reduction in Salmonella in a stockpile, while this would be fully achieved within 1 week in a sleeve. This approach of combining high-resolution spatial field sampling along with decay rates of pathogens under controlled lab conditions may improve quantitative microbial risk assessments and future regulations of manure utilization.

Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.

Application of Photogrammetry Technique for quarry stockpile estimation

The quarrying activities is one of the largest industries in the world which supplied aggregate primarily for construction of any buildings and structures. Continuous supply of aggregates is very important to ensure the construction activities can be carried out without delays. Hence, the quarry operators consistently monitor their stockpile volume to meet the client’s demands. In most cases, the determination of available stockpile at the quarry are done by utilizing conventional method (manual measurement of the stockpile’s dimension). This approach is time consuming and sometimes required professional surveyor to carry out the task. Hence in this work, a comparative study between conventional and photogrammetry method was done to estimate the stockpile in a quarry. Drone was flying to capture the aerial images of a stockpile in the quarry. The effect of the flying height and the percentage of overlapping on the accuracy of stockpile volume was studied. Result shows at lower percent of side overlap (50%), the accuracy of estimation is better. The difference between the photogrammetry technique and conventional method only 2.5%. It can be concluded that photogrammetry technique is very reliable to be applied by the quarry operators to estimate their stockpile volume.

Charcoal heaps volume estimation based on unmanned aerial vehicles

The charcoal stock in a forestry business is controlled based on the theoretical capacity of the masonry ovens (input) and shipped trucks (output). During the year, the company must monitor the stock for accountability reports and internal governance. This paper proposes a more efficient and precise survey method that overcomes the challenges of the common monitoring system in Brazil. In this study, a monitoring method based on digital stereoscopy from UAV images was implemented and evaluated. The results were compared with those of the traditional topographic survey based on RTK equipment. A multi-engine UAV, with an integrated global navigation satellite system (GNSS) and real-time kinematic positioning (RTK) equipment was used to fly over and survey a masonry oven complex containing eight charcoal heaps. Two stereoscopic processing methods were applied: (1) very low quality and (2) high quality to image alignment, reconstruction of a dense cloud, facet count and a three-dimensional mesh creation. Low-quality products showed geometric deformities when compared to high quality, but resulted in estimations similar to the topographic survey. Results indicated that the volume estimation of the charcoal heaps using UAV derived orthomosaics can replace the conventional method of GNSS RTK surveys with considerable gains in stockpile volume accuracy, inventory frequency and labour safety. The high quality processing method registered improvements in geometric precision and accuracy.

Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation

The accuracy of stockpile estimations is of immense criticality to process optimisation and overall financial decision making within manufacturing operations. Despite well-established correlations between inventory management and profitability, safe deployment of stockpile measurement and inspection activities remain challenging and labour-intensive. This is perhaps owing to a combination of size, shape irregularity as well as the health hazards of cement manufacturing raw materials and products. Through a combination of simulations and real-life assessment within a fully integrated cement plant, this study explores the potential of drones to safely enhance the accuracy of stockpile volume estimations. Different types of LiDAR sensors in combination with different flight trajectory options were fully assessed through simulation whilst mapping representative stockpiles placed in both open and fully confined areas. During the real-life assessment, a drone was equipped with GPS for localisation, in addition to a 1D LiDAR and a barometer for stockpile height estimation. The usefulness of the proposed approach was established based on mapping of a pile with unknown volume in an open area, as well as a pile with known volume within a semi-confined area. Visual inspection of the generated stockpile surface showed strong correlations with the actual pile within the open area, and the volume of the pile in the semi-confined area was accurately measured. Finally, a comparative analysis of cost and complexity of the proposed solution to several existing initiatives revealed its proficiency as a low-cost robotic system within confined spaces whereby visibility, air quality, humidity, and high temperature are unfavourable.

Comparative Analysis of Stockpile Volume Estimation using UAV and GPS Techniques

Mining operations involve the extraction of minerals of economic value from the earth. In surface mining operations, overburdens need to be stripped in other to reach the ore. Large volumes of waste as well as ore is stripped in the process. Various technologies have been used to aid in stockpile volume estimation. Notable among them are the Total Stations and Global Positioning Systems (GPS). However, labour, safety and time has challenged the use of these technologies. Unmanned Aerial Vehicle (UAV), commonly known as drone is an emerging technology for stockpile volume computations in the Mine. UAV technology for data collection is less labour intensive, safer and faster. Therefore, this study applied the UAV technology in an open pit to estimate stockpile volumes from a Mine. For the purpose of this study, GPS and UAV data were collected for measuring stockpile volumes of materials mined. The actual volumes of stockpiles A, B, C, D (Case 2), produced differences of 0.05% for A, 0.05% for B, 0.08% for C, 0.07% for D and 0.03% for A, -0.03% for B, 0.03% for C and 0.04% for D, for the GPS-based and the UAV-based techniques, respectively. The GPS-based technique generated moderate accuracies for volume estimation, but was time consuming and labour intensive, compared to the UAV-based technique; which was faster and less labour intensive. The UAV-based technique was the most accurate, safest and is capable of mapping large areas rapidly. It is therefore recommended that UAV survey be incorporated in stockpile volume estimation.
 Keywords: UAV, GPS, Stockpile, Mine, Total Stations

An aerial robotic system for inventory of stockpile warehouses

AbstractThis article describes the development and evaluation of an aerial robotic system for smart inventory of stockpile warehouses. The system was developed to automatically measure piles of different bulk materials, such as phosphorus and potassium compounds, which are stored in bays of different sizes inside a warehouse. This warehouse configuration is very common among fertilizer and animal food industries in Brazil. While an inventory can be executed by a human technician, the insalubrious environment, the imprecision of the manual volume estimation, and the time spent by the technician to access the information motivate the automation of the process. The proposed system uses a multirotor electrical drone that navigates autonomously inside the warehouse while it acquires light detection and ranging (LIDAR) point cloud data. This data is used to build a three‐dimensional (3D) model of the environment, which is then processed to identify the stockpiles of material and calculate their volumes. Since the environment is GPS‐denied and its characteristics, including symmetry, illumination and texture, do not favor visual‐ or LIDAR‐based localization, a drone navigation strategy that relies on relative positioning with respect to simple structures of the warehouse was developed. This article also presents our approach for autonomous stockpile volume estimation, which was numerically evaluated both in simulation and with real data, yielding in accuracy and precision of about 98%. The results presented in the article show that the aerial system is able to substitute the previously adopted manual procedure, highly increasing its accuracy, repeatably and safety, and drastically reducing its time of execution and cost.