{"title":"The Analysis of Stereo Vision 3D Point Cloud Data of Autonomous Vehicle Obstacle Recognition","authors":"Li Pei, Zhou Rui","doi":"10.1109/IHMSC.2015.192","DOIUrl":null,"url":null,"abstract":"In order to solve the problem of 3D point cloud data interpretation for the autonomous vehicle avoidance system, based on the analysis of the characteristics of the point cloud data, an analysis method of point grid projection is presented in this paper. This method can effectively obtain the interpretation of the road obstacles from the point cloud data. In this paper, the error and independent point was first analyzed and filtered out. Then road area of interest is divided into grid. Useful points are projected onto the grid and the projection effect would give a comprehensive criterion for interpretation, and location of the road obstacles. The method is verified by experiments and the experimental results show that, this method can effectively assist the autonomous vehicle navigation system to determine the obstacles.","PeriodicalId":6592,"journal":{"name":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"4 1","pages":"207-210"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2015.192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In order to solve the problem of 3D point cloud data interpretation for the autonomous vehicle avoidance system, based on the analysis of the characteristics of the point cloud data, an analysis method of point grid projection is presented in this paper. This method can effectively obtain the interpretation of the road obstacles from the point cloud data. In this paper, the error and independent point was first analyzed and filtered out. Then road area of interest is divided into grid. Useful points are projected onto the grid and the projection effect would give a comprehensive criterion for interpretation, and location of the road obstacles. The method is verified by experiments and the experimental results show that, this method can effectively assist the autonomous vehicle navigation system to determine the obstacles.
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