Salt Stockpile Inventory Management Using LiDAR Volumetric Measurements

Mahlberg, Justin; Manish, Raja; Koshan, Yerassyl; Joseph, Mina; Liu, Jidong; Wells, Timothy; McGuffey, Jeremy; Habib, Ayman; Bullock, Darcy M.

doi:10.3390/rs14194802

Cited by 9 publications

(17 citation statements)

References 20 publications

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“…Mahlberg et al [13] pioneered a portable LiDAR device mounted on a pole for scanning point clouds in expansive granaries. However, this method relies on complex point cloud registration techniques, leading to considerable evaluation complexity.…”

Section: D Reconstruction Of Coal Pilesmentioning

confidence: 99%

“…To fill gaps in the point cloud, we incrementally move 5 cm along the x-and y-axes, using Formula (12) to generate new points for the surface. The resultant point cloud is commonly represented as follows: (13) where P m represents the point cloud after being fitted by the least squares method, and M m denotes the number of points in the fitted P m .…”

Section: Moving Least Squaresmentioning

confidence: 99%

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A Novel Multi-LiDAR-Based Point Cloud Stitching Method Based on a Constrained Particle Filter

Ji,

He,

et al. 2024

Electronics

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In coal-fired power plants, coal piles serve as the fundamental management units. Acquiring point clouds of coal piles facilitates the convenient measurement of daily coal consumption and combustion efficiency. When using servo motors to drive Light Detection and Ranging (LiDAR) scanning of large-scale coal piles, the motors are subject to rotational errors due to gravitational effects. As a result, the acquired point clouds often contain significant noise. To address this issue, we proposes a Rapid Point Cloud Stitching–Constrained Particle Filter (RPCS-CPF) method. By introducing random noise to simulate servo motor rotational errors, both local and global point clouds are sequentially subjected to RPCS-CPF operations, resulting in smooth and continuous coal pile point clouds. Moreover, this paper presents a coal pile boundary detection method based on gradient region growing clustering. Experimental results demonstrate that our proposed RPCS-CPF method can generate smooth and continuous coal pile point clouds, even in the presence of servo motor rotational errors.

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Section: D Reconstruction Of Coal Pilesmentioning

confidence: 99%

Section: Moving Least Squaresmentioning

confidence: 99%

A Novel Multi-LiDAR-Based Point Cloud Stitching Method Based on a Constrained Particle Filter

Ji,

He,

et al. 2024

Electronics

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“…Similar to the practice adopted with TLS, several studies have mounted scanning sensors on tripods to scan stockpiles from multiple points. Mahlberg et al [107] developed an image/LiDAR-based system for indoor stockpile volume estimation, called the SMART (Stockpile Management and Reporting Technology) system. The system was applied to over 30 facilities to monitor salt usage during the winter season in the USA.…”

Section: Tripod-mounted Lidar Sensorsmentioning

confidence: 99%

Stockpile Volume Estimation in Open and Confined Environments: A Review

Alsayed,

Nabawy

2023

Drones

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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.

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“…As a solution to the abovementioned limitations of imaging and LiDAR mapping technologies, a camera-assisted LiDAR mapping platform, denoted as Stockpile Monitoring and Reporting Technology (SMART), has been developed [11,12]. The cost-effective design of the SMART system allows for fast and simple data collection of salt stockpiles with varying sizes and shape complexities.…”

Section: Introductionmentioning

confidence: 99%

“…Extracted conjugate features were then used in a LSA engine for the derivation of registration and feature parameters. In the previous work dealing with SMART [11,12], planar features were used for registering point clouds acquired in rectangular salt storage facilities. However, these geometric features do not exist when dealing with stockpiles within dome storage facilities.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

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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.

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Salt Stockpile Inventory Management Using LiDAR Volumetric Measurements

Cited by 9 publications

References 20 publications

A Novel Multi-LiDAR-Based Point Cloud Stitching Method Based on a Constrained Particle Filter

A Novel Multi-LiDAR-Based Point Cloud Stitching Method Based on a Constrained Particle Filter

Stockpile Volume Estimation in Open and Confined Environments: A Review

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

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