Filtering of LiDAR Point Cloud Data Based on new TIN Algorithm

Sui, Lichun; Zhu, Jianfeng; Zhu, Haixiong; Zhong, Mianqing

doi:10.2991/icmse-17.2017.14

Cited by 2 publications

(1 citation statement)

References 1 publication

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“…Zhang and Lin [7] used the smooth constraint segmentation method to reduce the type I error for PTD which cannot protect the ground details in steep terrain. Sui [8] et al arranged the point clouds in each triangulation network in ascending order, and then iterated the TIN according to the order of sorting, thus effectively reducing the type II error and improving the reliability of filtering. The process principle is shown below.…”

Section: Progressive Tin Densificationmentioning

confidence: 99%

Comparison and analysis of typical laser point cloud filtering algorithms

Wang,

Ma,

Dai

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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Laser radar is widely used in terrain mapping, unmanned driving and other fields because of its characteristics of accurate ranging, high sensitivity and all-weather work. However, more ground point clouds have a negative impact on the accuracy and speed of salient object detection. How to use laser point cloud filtering technology to efficiently extract ground points and non-ground point areas has become the key to improve the calculation speed and reduce the amount of computation. Therefore, this paper takes the SemanticKITTI dataset as the research object and combines theory with experiment to compare the effectiveness of the three-laser point cloud filtering algorithms of current mainstream morphology, CSF and PTD. The research work has guiding significance for scientifically selecting effective laser filtering algorithms.

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Section: Progressive Tin Densificationmentioning

confidence: 99%

Comparison and analysis of typical laser point cloud filtering algorithms

Wang,

Ma,

Dai

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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A Novel Ground Filtering Method for Point Clouds in a Forestry Area Based on Local Minimum Value and Machine Learning

Sang

Wang

2022

Applied Sciences

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Lidar point cloud filtering is the process of separating ground points from non-ground points and is a particularly important part of point cloud data processing. Forest filtering has always been a difficult topic in point cloud filtering research. Given that vegetation cannot be completely summarized according to the structure of ground objects, and given the diversity and complexity of the terrain in woodland areas, filtering in the forest area is a particularly difficult task. However, only few studies have tested the application of the point cloud filtering method for forest areas, the parameter setting of filtering methods is highly complex, and their terrain adaptability is weak. This paper proposes a new filtering method for forest areas that effectively combines iterative minima with machine learning, thereby greatly reducing the degree of manual participation. Through filtering tests on three types of woodlands, the filtering results were evaluated based on the filtering error definition proposed by ISPRS and were compared with the filtering results of other classical methods. Experimental results highlight the advantages of the proposed method, including its high accuracy, strong terrain universality, and limited number of parameters.

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Filtering of LiDAR Point Cloud Data Based on new TIN Algorithm

Cited by 2 publications

References 1 publication

Comparison and analysis of typical laser point cloud filtering algorithms

Comparison and analysis of typical laser point cloud filtering algorithms

A Novel Ground Filtering Method for Point Clouds in a Forestry Area Based on Local Minimum Value and Machine Learning

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