Expressway Surface Point Extraction from Mobile Laser Scanning Point Clouds

Liu, Rufei; Mao, Tian; Shi, Bo; Xu, Junyi

doi:10.25103/jestr.062.04

Cited by 8 publications

(13 citation statements)

References 8 publications

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“…Petzold et al [ 21 ] proposed the moving window least squares fitting algorithm, which fits the surface according to the lowest point of each window and eliminates the points with distance to the surface exceeding the threshold. Liu et al [ 22 ] proposed a denoising algorithm for pavement point cloud data by a vehicle-borne scanner. The noise points are identified and eliminated through the cluster analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Qiu et al [ 25 ] utilized the neighborhood to eliminate the scattered noise points. Liu et al [ 22 ] and Qiu et al [ 25 ] mainly aimed at denoising vehicle scanned point cloud. The density and accuracy of the scanned points are greatly affected by the vehicle speed and the road condition, which makes these algorithms not suitable for denoising the point cloud data obtained under high accuracy measurement.…”

Section: Introductionmentioning

confidence: 99%

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Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection

Chen

Wei

Xiao

et al. 2021

Sensors

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As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in the precision loss during the 3D reconstruction. The commonly used denoising algorithms ignore the strong planarity feature of the pavement, and thus might mistakenly eliminate ground points. This study proposes an ellipsoid detection algorithm to emphasize the planarity feature of the pavement during the 3D scanned data denoising process. By counting neighbors within the ellipsoid neighborhood of each point, the threshold of each point can be calculated to distinguish if it is the ground point or the noise point. Meanwhile, to narrow down the detection space and to reduce the processing time, the proposed algorithm divides the cloud point into cells. The result proves that this denoising algorithm can identify and eliminate the scattered noise points and the foreign body noise points very well, providing precise data for later 3D reconstruction of the scanned pavement.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection

Chen

Wei

Xiao

et al. 2021

Sensors

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“…For example, by means of the widely used triangular irregular network (TIN) progressive filtering method, reference (Fang et al, 2015) extract terrain patch segmentation. The other TIN-based methods can be found in literature (Wei et al,2014;Liu et al,2015) . Iterative processing is feasible for airborne LiDAR data because of relatively low point density.…”

Section: Introductionmentioning

confidence: 99%

A Voxel-Based Filtering Algorithm for Mobile Lidar Data

Qin

Guan

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract:This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

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“…They used trajectory information and performed 2D segmentation to filter the ground points. Liu et al (2013) and Tian et al (2014) proposed similar three step model for ground extraction. Three dimensional grids were generated using vehicle trajectory, point density and slope.…”

Section: Introductionmentioning

confidence: 99%

“…The current researches on mobile or terrestrial point cloud data focus on independent object extraction (Liu et al, 2013;Tian et al, 2014). Yu et al (2014) used block based elevation method for ground filtering.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Ground Surface Along Roadway From Mobile Lidar Data

Yadav

Singh

Lohani

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. High quality digital elevation model (DEM) is obtained from mobile LiDAR data and it is used in various applications like road widening, slope measurement of road side surfaces, and inundation of the roadway evaluation. Two steps algorithm is proposed to filter ground points using mobile LiDAR data. Initially unstructured input data is organized then standard deviation and flatness based approach is used to filter ground points. Proposed algorithm is tested on point cloud of test site located along 800m of roadway. Type I, Type II and total error are 2.11%, 2.21% and 2.15%, respectively with kappa is equal to 96.61% are computed using ground filtered points and reference data points.

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Expressway Surface Point Extraction from Mobile Laser Scanning Point Clouds

Cited by 8 publications

References 8 publications

Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection

Pavement 3D Data Denoising Algorithm Based on Cell Meshing Ellipsoid Detection

A Voxel-Based Filtering Algorithm for Mobile Lidar Data

Extraction of Ground Surface Along Roadway From Mobile Lidar Data

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