Point Cloud Denoising Processing Technology for Complex Terrain Debris Flow Gully

Yang, Min; Gan, Shu; Yuan, Xing; Yu, Hui; Qiu, Pengrui; Zhan, Qiao

doi:10.1109/icccbda.2019.8725725

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To precisely locate the negative obstacle point cloud location, we needed to use the PassThrough filter in PCL (Point Cloud Learning) to filter out the point cloud data other than the negative obstacle to facilitate our experiment's continuation [23].…”

Section: Passthrough Filter To Locate Point Clouds At Negative Obstaclesmentioning

confidence: 99%

Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud

Zhi-ming

Chen

et al. 2021

Information

View full text Add to dashboard Cite

A single VLP-16 LiDAR estimation method based on a single-frame 3D laser point cloud is proposed to address the problem of estimating negative obstacles’ geometrical features in structured environments. Firstly, a distance measurement method is developed to determine the estimation range of the negative obstacle, which can be used to verify the accuracy of distance estimation. Secondly, the 3D point cloud of a negative obstacle is transformed into a 2D elevation raster image, making the detection and estimation of negative obstacles more intuitive and accurate. Thirdly, we compare the effects of a StatisticalOutlierRemoval filter, RadiusOutlier removal, and Conditional removal on 3D point clouds, and the effects of a Gauss filter, Median filter, and Aver filter on 2D image denoising, and design a flowchart for point cloud and image noise reduction and denoising. Finally, a geometrical feature estimation method is proposed based on the elevation raster image. The negative obstacle image in the raster is used as an auxiliary line, and the number of pixels is derived from the OpenCV-based Progressive Probabilistic Hough Transform to estimate the geometrical features of the negative obstacle based on the raster size. The experimental results show that the algorithm has high accuracy in estimating the geometric characteristics of negative obstacles on structured roads and has a practical application value for LiDAR environment perception research.

show abstract

Section: Passthrough Filter To Locate Point Clouds At Negative Obstaclesmentioning

confidence: 99%

Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud

Zhi-ming

Chen

et al. 2021

Information

View full text Add to dashboard Cite

show abstract

Point Cloud Denoising Processing Technology for Complex Terrain Debris Flow Gully

Cited by 1 publication

References 2 publications

Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud

Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud

Contact Info

Product

Resources

About