A novel background filtering method for LiDAR based on point cloud ground line segmentation

2021 ◽  
Author(s):  
Xiuguang Song ◽  
Zhaoyou Ma ◽  
Chunyu Zhou ◽  
Fei Cheng ◽  
Xucai Zhuang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Filtering Method ◽  
Line Segmentation ◽  
Ground Line

scholarly journals 3D point cloud filtering method for pose measurement application of space non-cooperative targets

2019 ◽  
Vol 40 (2) ◽  
pp. 24-28
Author(s):  
GU Yingying ◽  
WANG Li ◽  
HUA Baocheng ◽  
LIU Da ◽  
WU Yun ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Filtering Method

An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area

2021 ◽  
pp. 1-1
Author(s):  
Panfeng Yang ◽  
Haiqiang Fu ◽  
Jianjun Zhu ◽  
Yi Li ◽  
Changcheng Wang
Keyword(s):  
Point Cloud ◽  
Forest Area ◽  
Filtering Method

scholarly journals A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

2019 ◽  
Vol 11 (2) ◽  
pp. 198 ◽  
Author(s):  
Chunhua Hu ◽  
Zhou Pan ◽  
Pingping Li
Keyword(s):  
Leaf Area ◽  
Point Cloud ◽  
Mean Absolute Error ◽  
Point Clouds ◽  
Absolute Error ◽  
Area Measurement ◽  
3D Point Cloud ◽  
Filtering Method ◽  
Normal Estimation ◽  
3D Point Clouds

Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm2. The MAE values for small leaves, medium leaves and large leaves were 0.92 cm2, 1.05 cm2 and 3.39 cm2, respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.

scholarly journals Research on Point Cloud Registering Method of Tunneling Roadway Based on 3D NDT-ICP Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4448
Author(s):  
Jianjian Yang ◽  
Chao Wang ◽  
Wenjie Luo ◽  
Yuchen Zhang ◽  
Boshen Chang ◽  
...  
Keyword(s):  
Coordinate Transformation ◽  
Point Cloud ◽  
Point Clouds ◽  
Icp Algorithm ◽  
Point Pair ◽  
Filtering Method ◽  
Environmental Features ◽  
Modeling Accuracy ◽  
Nonlinear Objective Function ◽  
Voxel Grid

In order to meet the needs of intelligent perception of the driving environment, a point cloud registering method based on 3D NDT-ICP algorithm is proposed to improve the modeling accuracy of tunneling roadway environments. Firstly, Voxel Grid filtering method is used to preprocess the point cloud of tunneling roadways to maintain the overall structure of the point cloud and reduce the number of point clouds. After that, the 3D NDT algorithm is used to solve the coordinate transformation of the point cloud in the tunneling roadway and the cell resolution of the algorithm is optimized according to the environmental features of the tunneling roadway. Finally, a kd-tree is introduced into the ICP algorithm for point pair search, and the Gauss–Newton method is used to optimize the solution of nonlinear objective function of the algorithm to complete accurate registering of tunneling roadway point clouds. The experimental results show that the 3D NDT algorithm can meet the resolution requirement when the cell resolution is set to 0.5 m under the condition of processing the point cloud with the environmental features of tunneling roadways. At this time, the registering time is the shortest. Compared with the NDT algorithm, ICP algorithm and traditional 3D NDT-ICP algorithm, the registering speed of the 3D NDT-ICP algorithm proposed in this paper is obviously improved and the registering error is smaller.

scholarly journals TUNNEL POINT CLOUD FILTERING METHOD BASED ON ELLIPTIC CYLINDRICAL MODEL

2016 ◽  
Vol XLI-B1 ◽  
pp. 735-740
Author(s):  
Ningning Zhu ◽  
Yonghong Jiaa ◽  
Lun Luo
Keyword(s):  
Horizontal Plane ◽  
Point Cloud ◽  
Deformation Monitoring ◽  
Metal Stents ◽  
Point Cloud Data ◽  
Cylindrical Model ◽  
Cloud Data ◽  
Filtering Method ◽  
Tunnel Section ◽  
Periodic Monitoring

The large number of bolts and screws that attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, make the laser point cloud data include lots of non-tunnel section points (hereinafter referred to as non-points), therefore affecting the accuracy for modeling and deformation monitoring. This paper proposed a filtering method for the point cloud based on the elliptic cylindrical model. The original laser point cloud data was firstly projected onto a horizontal plane, and a searching algorithm was given to extract the edging points of both sides, which were used further to fit the tunnel central axis. Along the axis the point cloud was segmented regionally, and then fitted as smooth elliptic cylindrical surface by means of iteration. This processing enabled the automatic filtering of those inner wall non-points. Experiments of two groups showed coincident results, that the elliptic cylindrical model based method could effectively filter out the non-points, and meet the accuracy requirements for subway deformation monitoring. The method provides a new mode for the periodic monitoring of tunnel sections all-around deformation in subways routine operation and maintenance.

Filtering method for 3D laser scanning point cloud

2015 ◽  
Author(s):  
Da Liu ◽  
Li Wang ◽  
Yuncai Hao ◽  
Jun Zhang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Laser Scanning ◽  
Filtering Method

scholarly journals Segmentation-Based Ground Points Detection from Mobile Laser Scanning Point Cloud

2015 ◽  
Vol XL-7/W4 ◽  
pp. 99-102 ◽  
Author(s):  
X. Lin ◽  
J. Zhang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Total Error ◽  
Omission Error ◽  
Commission Error ◽  
Processing Unit ◽  
Triangulated Irregular Network ◽  
Filtering Method ◽  
Individual Point

In most Mobile Laser Scanning (MLS) applications, filtering is a necessary step. In this paper, a segmentation-based filtering method is proposed for MLS point cloud, where a segment rather than an individual point is the basic processing unit. Particularly, the MLS point cloud in some blocks are clustered into segments by a surface growing algorithm, then the object segments are detected and removed. A segment-based filtering method is employed to detect the ground segments. Two MLS point cloud datasets are used to evaluate the proposed method. Experiments indicate that, compared with the classic progressive TIN (Triangulated Irregular Network) densification algorithm, the proposed method is capable of reducing the omission error, the commission error and total error by 3.62%, 7.87% and 5.54% on average, respectively.

Sign in / Sign up

Export Citation Format

Share Document