Open AccessObstacle Detection Based on 3D Lidar Euclidean Clustering
Author(s) -
JinMing Chen
Publication year - 2021
Publication title -
applied science and innovative research
Language(s) - English
Resource type - Journals
eISSN - 2474-4980
pISSN - 2474-4972
DOI - 10.22158/asir.v5n3p39
Subject(s) - obstacle , point cloud , cluster analysis , computer vision , computer science , artificial intelligence , lidar , segmentation , euclidean distance , outlier , point (geometry) , remote sensing , geography , mathematics , archaeology , geometry
Environment perception is the basis of unmanned driving and obstacle detection is an important research area of environment perception technology. In order to quickly and accurately identify the obstacles in the direction of vehicle travel and obtain their location information, combined with the PCL (Point Cloud Library) function module, this paper designed a euclidean distance based Point Cloud clustering obstacle detection algorithm. Environmental information was obtained by 3D lidar, and ROI extraction, voxel filtering sampling, outlier point filtering, ground point cloud segmentation, Euclide clustering and other processing were carried out to achieve a complete PCL based 3D point cloud obstacle detection method. The experimental results show that the vehicle can effectively identify the obstacles in the area and obtain their location information.