An automatic hybrid method for ground filtering in mobile laser scanning data of various types of roadway environments

Yadav, Manohar; Khan, Parvej; Singh, Ajai K.; Lohani, Bharat

doi:10.1016/j.autcon.2021.103681

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…Taking 0.5 m as the threshold, PTD, CSF, and MES were used to extract ground points. For other parameters, we using the automated ground filtering in the [27], the optimal parameters were determined for each ground extraction method based on swarm intelligence algorithms. As shown in table 3, The R EE and R OE of MES and CSF are not significantly different, but MES is lower than CSF.…”

Section: Further Validation Of Extraction Resultsmentioning

confidence: 99%

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

Li,

Wang,

Sun

et al. 2024

Meas. Sci. Technol.

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The appearance of unmanned aerial vehicle photogrammetry and airborne lidar makes it possible to obtain measurement data for complex terrains such as gullies and mountainous regions. However, extracting ground points from these abundant and massive measurement datasets is challenging. In traditional extractions, their essence is to determine the surfaces that can describe the terrain from the seed points in the grid and use them as the basis for separating non-ground points. For effective extraction, this study proposes a multisource elevations strategy (MES) obtaining robust seed points and reference surfaces. First, two-level extended grids were constructed as the basic units. Then, to select more robust values between measurement and interpolation elevations, an elevation-determination rule was established for seed points. After, based fitting and interpolation elevations of grid nodes, the correction range is determined and the elevation is corrected for reference surfaces. In two representative complex terrain areas, when non-ground points were marked as seed points, the MES effectively reduced the phenomenon of seed points moving away from the ground. Reference surfaces can also accurately represent the global change trend and local elevation of the ground in areas where the terrain changes rapidly. This strategy provides a new thinking for ground point extraction from point cloud.

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Section: Further Validation Of Extraction Resultsmentioning

confidence: 99%

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

Li,

Wang,

Sun

et al. 2024

Meas. Sci. Technol.

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show abstract

“…The light detection and ranging (LiDAR) is a powerful remote sensing technique that captures detailed 3D geometrical as well as some radiometric information of the objects in a scene [8]. The point density of the LiDAR-acquired point cloud is defined in angular resolution, and thus, the density on object surfaces declines with the object distance at the rate of 1/range 2 .…”

Section: Introductionmentioning

confidence: 99%

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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“…Therefore, effective road rehabilitation, maintenance, management, and planning are essential in smart urban applications. This requires high accuracy and up-to-date information regarding road surface and road geometry [1][2][3]. Road surface information is also required for increasing needs and applications such as ensuring road safety, road comfort [4], and high-accuracy navigation of self-drive vehicles.…”

Section: Introductionmentioning

confidence: 99%

Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model

Zeybek¹,

Biçici²

2023

Meas. Sci. Technol.

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Roads are one of the main characteristics of cities, and their data should be updated periodically. In this study, a new automatic method is proposed for extracting road surface information and road inventory from a Mobile LiDAR System-based point cloud. The proposed method consists of four steps. First, a three-dimensional point cloud is acquired using the MLS raw data. To improve the extraction accuracy, irrelevant points are removed from the point cloud. Piecewise linear models are used in the third step to classify the road surface. Road geometric characteristics such as centerline, profile, cross-section, and cross slope are extracted in the final step. The manually obtained road boundary is compared with the extracted road boundary to assess the classification results. Completeness, correctness, quality, and accuracy measures are range from 97% to 99%. When comparing these measures with previous studies, the proposed method produces one of the highest ones.

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An automatic hybrid method for ground filtering in mobile laser scanning data of various types of roadway environments

Cited by 11 publications

References 32 publications

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

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

Road surface and inventory extraction from mobile LiDAR point cloud using iterative piecewise linear model

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