Comparison of Unmanned Aerial Vehicle-LiDAR and Image-Based Mobile Mapping System for Assessing Road Geometry Parameters via Digital Terrain Models

Suleymanoglu, Baris; Gürtürk, Mert; Yilmaz, Yalcin; Soycan, Arzu; Soycan, Metin

doi:10.1177/03611981231157730

Cited by 2 publications

(4 citation statements)

References 49 publications

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“…To allow for PPK solution, a GNSS base station was installed in the test area. LiDAR data processing was conducted through the utilization of LiDARMill [ 71 ]. NavLab, a crucial feature of LiDARMill, was used to integrate the IMU and GNSS data, generating a precise and accurate trajectory that was used to create the LiDAR point cloud.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…GNSS/IMU data were processed using Inertial Explorer by NovAtel and, subsequently, used for georeferencing images and point cloud generation. The system calibration, time synchronization, georefer-encing of frames, and detailed system analysis can be found in [71,72]. The MMS platform, shown in Figure 13, was equipped with two GoPro Hero 7 action cameras, Xsens MTi-G-700 IMU and two Topcon HyperPro dual-frequency GNSS receivers; all mounted on the roof of a van.…”

Section: Experiments Study Areas Data Acquisition Systems and Datasetsmentioning

confidence: 99%

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3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Suleymanoglu,

Soycan,

Toth

2024

Sensors

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The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Experiments Study Areas Data Acquisition Systems and Datasetsmentioning

confidence: 99%

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3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Suleymanoglu,

Soycan,

Toth

2024

Sensors

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“…A roughness analysis of paved roads was carried out using drone LiDAR and photogrammetry [11], reporting 6 and 7.2 cm vertical accuracy in RMSE, respectively. A study used UAV-LiDAR and MPS to obtain geometric parameters (such as road longitudinal profile and cross-slope) by using digital terrain model surfaces derived from the point cloud data [12]. A study was conducted on earthwork volume calculation, and vertical accuracy of 5.4 and 2.5 cm were reported for photogrammetry and LiDAR, respectively [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Drone LiDAR Survey for Evaluation of a Long-Term Consolidation Settlement of Large Land Reclamation

Lee

2023

Applied Sciences

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Drone surveys are gaining popularity for many construction applications, including in the fields of civil engineering, such as road construction, earthwork, structure monitoring, and coastal topography analysis. Drone surveying has a high potential for periodical long-term ground settlement measurement in the field of geotechnical engineering. Traditionally, manual measurement has been performed for limited points with controlled surface measurement points, but drone surveying may enable automated and periodical measurement for a wide and remote site. However, the accuracy of the elevation measurement and the surface settlement prediction has not been investigated, and the use of drone surveying has thus been limited. Therefore, an experiment was carried out to apply drone LiDAR (Light Detection and Ranging) surveying for soft ground settlement measurement at a large land reclamation site showing a very large settlement up to 10 m. Periodic drone LiDAR surveying was conducted, and the data were processed with direct georeferencing and with outlier removals (such as trees and construction vehicles) in order to generate a clean surface point cloud. We then compared the processed elevation data with ground control data to check the vertical accuracy and to predict the settlement as well as for consolidation. The drone LiDAR survey showed 13 cm, 42.9 cm, and 6.23% differences in RMSE (Root Mean Square Error) in terms of vertical accuracy, predicted long-term settlement, and consolidation, respectively. The drone LiDAR accuracy seems very useful for monitoring settlement over a large and remote land reclamation site of soft ground, showing settlement up to several meters where, without a surface measurement, installment is limited.

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Comparison of Unmanned Aerial Vehicle-LiDAR and Image-Based Mobile Mapping System for Assessing Road Geometry Parameters via Digital Terrain Models

Cited by 2 publications

References 49 publications

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Application of Drone LiDAR Survey for Evaluation of a Long-Term Consolidation Settlement of Large Land Reclamation

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