scholarly journals AUTOMATIC EXTRACTION AND TOPOLOGY RECONSTRUCTION OF URBAN VIADUCTS FROM LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 131-135
Author(s):  
Y. Wang ◽  
X. Hu
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Airborne Lidar ◽  
Least Square ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Topological Graph ◽  
Cloud Data ◽  
City Model ◽  
Region Growth

Urban viaducts are important infrastructures for the transportation system of a city. In this paper, an original method is proposed to automatically extract urban viaducts and reconstruct topology of the viaduct network just with airborne LiDAR point cloud data. It will greatly simplify the effort-taking procedure of viaducts extraction and reconstruction. In our method, the point cloud first is filtered to divide all the points into ground points and none-ground points. Region growth algorithm is adopted to find the viaduct points from the none-ground points by the features generated from its general prescriptive designation rules. Then, the viaduct points are projected into 2D images to extract the centerline of every viaduct and generate cubic functions to represent passages of viaducts by least square fitting, with which the topology of the viaduct network can be rebuilt by combining the height information. Finally, a topological graph of the viaducts network is produced. The full-automatic method can potentially benefit the application of urban navigation and city model reconstruction.

scholarly journals HOLE-FILLING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 739-745
Author(s):  
P. Liang ◽  
G. Q. Zhou ◽  
Y. L. Lu ◽  
X. Zhou ◽  
B. Song
Keyword(s):  
Boundary Point ◽  
Point Cloud ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Airborne Lidar ◽  
Least Square ◽  
Point Cloud Data ◽  
K Nearest Neighbor ◽  
Cloud Data ◽  
Moving Least Square

Abstract. Due to the influence of the occlusion of objects or the complexity of the measured terrain in the scanning process of airborne lidar, the point cloud data inevitably appears holes after filtering and other processing. The incomplete data will inevitably have an impact on the quality of the reconstructed digital elevation model, so how to repair the incomplete point cloud data has become an urgent problem to be solved. To solve the problem of hole repair in point cloud data, a hole repair algorithm based on improved moving least square method is proposed in this paper by studying existing hole repair algorithms. Firstly, the algorithm extracts the boundary of the point cloud based on the triangular mesh model. Then we use k-nearest neighbor search to obtain the k-nearest neighbor points of the boundary point. Finally, according to the boundary point and its k-nearest neighbor point, the improved moving least squares method is used to fit the hole surface to realize the hole repair. Combined with C++ and MATLAB language, the feasibility of the algorithm is tested by specific application examples. The experimental results show that the algorithm can effectively repair the point cloud data holes, and the repairing precision is high. The filled hole area can be smoothly connected with the boundary.

ESTIMATING SURFACE NORMALS IN NOISY POINT CLOUD DATA

2004 ◽  
Vol 14 (04n05) ◽  
pp. 261-276 ◽  
Author(s):  
NILOY J. MITRA ◽  
AN NGUYEN ◽  
LEONIDAS GUIBAS
Keyword(s):  
Point Cloud ◽  
Smooth Surface ◽  
Smooth Curve ◽  
Local Information ◽  
Least Square ◽  
Neighborhood Size ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Normal Estimation ◽  
Least Square Fitting

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ2or a smooth surface in ℝ3, and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.

scholarly journals Automatic extraction of pavement markings on streets from point cloud data of mobile LiDAR

2017 ◽  
Vol 28 (8) ◽  
pp. 085203 ◽  
Author(s):  
Yang Gao ◽  
Ruofei Zhong ◽  
Tao Tang ◽  
Liuzhao Wang ◽  
Xianlin Liu
Keyword(s):  
Point Cloud ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Pavement Markings

scholarly journals General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

2019 ◽  
Vol 11 (23) ◽  
pp. 2737 ◽  
Author(s):  
Minsu Kim ◽  
Seonkyung Park ◽  
Jeffrey Danielson ◽  
Jeffrey Irwin ◽  
Gregory Stensaas ◽  
...  
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Conjugate Point ◽  
Airborne Lidar ◽  
Lidar Data ◽  
General Function ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Uncertainty Model ◽  
External Uncertainty

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.

scholarly journals A posture measurement approach for an articulated manipulator by RGB-D cameras

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983813
Author(s):  
Haobin Shi ◽  
Meng Xu ◽  
Kao-Shing Hwang ◽  
Chia-Hung Hung
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Consensus Method ◽  
Average Error ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Sample Standard Deviation ◽  
Cylindrical Model ◽  
Cloud Data ◽  
Posture Measurement

The objective of this article aims at the safety problems where robots and operators are highly coupled in a working space. A method to model an articulated robot manipulator by cylindrical geometries based on partial cloud points is proposed in this article. Firstly, images with point cloud data containing the posture of a robot with five resolution links are captured by a pair of RGB-D cameras. Secondly, the process of point cloud clustering and Gaussian noise filtering is applied to the images to separate the point cloud data of three links from the combined images. Thirdly, an ideal cylindrical model fits the processed point cloud data are segmented by the random sample consensus method such that three joint angles corresponding to three major links are computed. The original method for calculating the normal vector of point cloud data is the cylindrical model segmentation method, but the accuracy of posture measurement is low when the point cloud data is incomplete. To solve this problem, a principal axis compensation method is proposed, which is not affected by number of point cloud cluster data. The original method and the proposed method are used to estimate the three joint angular of the manipulator system in experiments. Experimental results show that the average error is reduced by 27.97%, and the sample standard deviation of the error is reduced by 54.21% compared with the original method for posture measurement. The proposed method is 0.971 piece/s slower than the original method in terms of the image processing velocity. But the proposed method is still feasible, and the purpose of posture measurement is achieved.

scholarly journals A Workflow to Extract the Geometry and Type of Vegetated Landscape Elements from Airborne LiDAR Point Clouds

2021 ◽  
Vol 13 (20) ◽  
pp. 4031
Author(s):  
Ine Rosier ◽  
Jan Diels ◽  
Ben Somers ◽  
Jos Van Orshoven
Keyword(s):  
River Discharge ◽  
Point Cloud ◽  
Point Clouds ◽  
Flood Frequency ◽  
Airborne Lidar ◽  
Main Function ◽  
Point Cloud Data ◽  
Landscape Elements ◽  
Cloud Data ◽  
Landscape Element

Rural European landscapes are characterized by a variety of vegetated landscape elements. Although it is often not their main function, they have the potential to affect river discharge and the frequency, extent, depth and duration of floods downstream by creating both hydrological discontinuities and connections across the landscape. Information about the extent to which individual landscape elements and their spatial location affect peak river discharge and flood frequency and severity in agricultural catchments under specific meteorological conditions is limited. This knowledge gap can partly be explained by the lack of exhaustive inventories of the presence, geometry, and hydrological traits of vegetated landscape elements (vLEs), which in turn is due to the lack of appropriate techniques and source data to produce such inventories and keep them up to date. In this paper, a multi-step methodology is proposed to delineate and classify vLEs based on LiDAR point cloud data in three study areas in Flanders, Belgium. We classified the LiDAR point cloud data into the classes ‘vegetated landscape element point’ and ‘other’ using a Random Forest model with an accuracy classification score ranging between 0.92 and 0.97. The landscape element objects were further classified into the classes ‘tree object’ and ‘shrub object’ using a Logistic Regression model with an area-based accuracy ranging between 0.34 and 0.95.

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