Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation

Alsayed, Ahmad; Yunusa‐Kaltungo, Akilu; Quinn, Mark K.; Arvin, Farshad; Nabawy, Mostafa R. A.

doi:10.3390/rs13173356

Cited by 19 publications

(7 citation statements)

References 63 publications

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“…Within the state of Indiana (USA), 60% of the salt storage facilities are domes. In recent years, stockpile volume estimation has been conducted using photogrammetric and LiDAR-based approaches [2][3][4][5][6][7]. Image-based techniques use automatically identified conjugate points in overlapping images for deriving a 3D model covering the area of interest through Structure from Motion (SfM) strategies [8].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Nowadays, numerous industries are embracing technological advancements such as drone technology instead of conventional surveying methods. Drones offer a cost-effective, safe, and rapid solution for aerial surveys and data collection, proving particularly useful for industries such as agriculture, civil engineering, and mining, which require constant monitoring [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Drones for the Analysis of Agricultural Land Use in the Mining Lease

Kaushal

Bhatnagar

2023

IJECC

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The utilization of artificial intelligence (AI) has facilitated the automation of drone control, which includes the management of navigation and movement. This application can be accomplished through several methods, including GPS tracking, computer vision, and machine learning algorithms. Drones exhibit a distinctive combination of spatial coverage and resolution, rendering them indispensable for land survey and mapping. The incorporation of multiple ground-control points has the potential to yield high precision georeferencing for the Orthomosaic product. In conjunction with field observations, drones provide a prompt and precise means of recording land data and its use. A drone survey and mapping operation was conducted within a mining lease situated near the village of Kanthariya, in the Tehsil and District of Chittorgarh, covering an area of 64.75 hectares, for the analysis of agricultural land use in the mining lease.

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“…Currently, numerous indoor localisation techniques have been investigated on UAV positioning in extremely confined environments. Among them, vision [4]- [6] and light detection and ranging (LiDAR) [7]- [12] based localisation techniques are known as the much-sought techniques in this area, owing to the enhanced localisation performance. For vision based techniques, Krul et al [5] proposed a visual simultaneous localisation and mapping (VSLAM) based system which achieved precise positioning of the DJI Tello for indoor livestock and farming.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, the increasing energy consumption leads by the additional laser scanner may reduce the UAV operation time. Furthermore, authors in [9]- [12] all utilised LiDAR based localisation techniques as the main components to achieve precise UAV positioning in confined environments for smart inspection, classification and disaster management. Nevertheless, the weight, high energy consumption and cost for the LiDAR based system turn into an inevitable problem when designing the UAV system.…”

Section: Introductionmentioning

confidence: 99%

High-Precision UWB-Based Localisation for UAV in Extremely Confined Environments

Yang

et al. 2022

IEEE Sensors J.

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In this paper, a high-precision ultra-wideband (UWB) based unmanned aerial vehicle (UAV) localisation approach is proposed for applications in extremely confined environments. It is motivated by the emerging demand on autonomous inspection in such environments that are hard or impossible for humans to access. Instead of the traditional localisation techniques such as global positioning system (GPS), vision based or other localisation techniques, the UWB based localisation technique is adopted for precise UAV positioning due to its high accuracy, implementation simplicity and suitability in such environments. To avoid the requirement on strict synchronisation between sensor nodes and provide decimetre-level accuracy, the proposed algorithm combined the two-way time-of-flight (TW-TOF) localisation scheme with the maximum likelihood estimation (MLE) method. This differs from applications in other environments, the number and deployment area of anchor nodes are highly restricted in such environments. Therefore, an in-depth investigation for the anchor deployment strategies is presented to find the most suitable geometry configurations with accurate and robust performance. Finally, extensive simulations, static experiments and flight tests have been conducted to validate the localisation performance under different deployment strategies. The experiments show that average localisation error and standard deviation (STD) under 0.2 m and 0.07 m are obtainable by using our proposed approach under three different geometry configurations of anchor nodes. This is suitable for different applications in extremely confined environments.

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Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation

Cited by 19 publications

References 63 publications

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

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

Application of Artificial Intelligence in Drones for the Analysis of Agricultural Land Use in the Mining Lease

High-Precision UWB-Based Localisation for UAV in Extremely Confined Environments

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