Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study

Lin, Yi-Chun; Liu, Jidong; Cheng, Yi-Ting; Hasheminasab, Seyyed Meghdad; Wells, Timothy; Bullock, Darcy M.; Habib, Ayman

doi:10.3390/s21227550

Cited by 7 publications

(7 citation statements)

References 67 publications

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“…First, an image-assisted coarse registration of the LiDAR scans is conducted wherein successive images are utilized to obtain scan-toscan transformation through constrained iterative matching of the scale invariant feature transform (SIFT) features in two successive images at a time. Once the LiDAR scans are coarsely registered, a final optimization routine based on least squares adjustment is initiated for a feature-based fine registration of all scans [31]. Finally, to compute the stockpile volume, the fine registered point clouds are leveled with the ground surface, and the boundary of the salt pile is defined.…”

Section: Overview Of Methodologymentioning

confidence: 99%

Statewide Implementation of Salt Stockpile Inventory Using LiDAR Measurements: Case Study

Mahlberg,

Malackowski,

Joseph

et al. 2024

Remote Sensing

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The state of Indiana maintains approximately 120 salt storage facilities strategically distributed across the state for winter operations. In April 2023, those facilities contained approximately 217,000 tons of salt with an estimated value of USD 21 million. Accurate inventories at each facility during the winter season are important for scheduling re-supply so the facilities do not run out of salt. Inventories are also important at the end of the season for restocking to provide balanced inventories. This paper describes the implementation of a portable pole-mounted LiDAR system to measure salt stockpile inventory at 120 salt storage facilities in Indiana. Using two INDOT staff members, the end-of-season inventory took 9 working days, with volumetric inventories provided within 24 h of data collection. To provide an independent evaluation of the methodologies, the Hovermap ST backpack was used at selected facilities to provide control volumes. This system has a range of 100 m and an accuracy of ±3 cm, which reduces the occlusion to less than 8%. The pre-season facility capacity ranged from 0% to 100%, with an average of 66% full across all facilities. The post-season facility percentage ranged from 3% to 100%, with an average of 70% full. In addition, permanent roof-mounted LiDAR systems were deployed at two facilities to evaluate the effectiveness of monitoring salt stockpile inventories during winter operation activities. Plans are now underway to install fixed LiDAR systems at 15 additional facilities for the 2023–2024 winter season.

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Section: Overview Of Methodologymentioning

confidence: 99%

Statewide Implementation of Salt Stockpile Inventory Using LiDAR Measurements: Case Study

Mahlberg,

Malackowski,

Joseph

et al. 2024

Remote Sensing

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“…In order to derive a fine-registered point cloud, planar feature matching is first conducted to establish corresponding features among all scans/stations. Finally, a planar feature-based LSA is adopted for simultaneous fine registration of all scans at all stations while solving for the parameters of the best-fitting planes using the approach proposed by Lin et al [31]. The fine registration results for the scans at the two stations in the Rensselaer dataset are shown…”

Section: A Initial Point Cloud Registrationmentioning

confidence: 99%

Linear Feature-Based Image/LiDAR Integration for a Stockpile Monitoring and Reporting Technology

Hasheminasab

Zhou

Habib

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Stockpile monitoring has been recently conducted with the help of modern remote sensing techniquese.g., terrestrial/aerial photogrammetry/LiDARthat can efficiently produce accurate 3D models for the area of interest. However, monitoring of indoor stockpiles still requires more investigation due to unfavorable conditions in these environments such as lack of global navigation satellite system (GNSS) signals and/or homogenous texture. This study develops a fully-automated image/LiDAR integration framework that is capable of generating accurate 3D models with color information for stockpiles under challenging environmental conditions. The derived colorized 3D point cloud can be subsequently used for volume estimation and visual inspection of stockpiles. The main contribution of the developed strategy is using automatically derived conjugate image/LiDAR linear features for simultaneous registration and camera/LiDAR system calibration. Data for this study is acquired using a camera-assisted LiDAR mapping platformdenoted as stockpile monitoring and reporting technology (SMART) -which was recently designed as a time-efficient and cost-effective bulk material tracking. Experimental results on three datasets show that the developed framework outperforms a classical planar feature-based registration technique in terms of the alignment of acquired point cloud. Results also indicate that the proposed approach can lead to a high relative accuracy between image lines and their corresponding back-projected LiDAR features in the range of 4-7 pixels.

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“…Some studies conducted feature-based registration by deploying special targets (e.g., highly reflective checkerboards and/or spherical targets) in the study site and then identifying them in different point clouds [23][24][25][26]. In order to increase the level of automation, several target-less registration approaches have been proposed that rely on natural geometric features (e.g., planar patches or linear features) in the area of interest [27][28][29][30][31]. For instance, Lin et al [27] extracted and matched planar, linear, and cylindrical features from point clouds acquired near a bridge.…”

Section: Introductionmentioning

confidence: 99%

“…In order to increase the level of automation, several target-less registration approaches have been proposed that rely on natural geometric features (e.g., planar patches or linear features) in the area of interest [27][28][29][30][31]. For instance, Lin et al [27] extracted and matched planar, linear, and cylindrical features from point clouds acquired near a bridge. Extracted conjugate features were then used in a LSA engine for the derivation of registration and feature parameters.…”

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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Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study

Cited by 7 publications

References 67 publications

Statewide Implementation of Salt Stockpile Inventory Using LiDAR Measurements: Case Study

Statewide Implementation of Salt Stockpile Inventory Using LiDAR Measurements: Case Study

Linear Feature-Based Image/LiDAR Integration for a Stockpile Monitoring and Reporting Technology

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

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