{"title":"基于双目视觉的室内移动机器人可驾驶区域快速检测","authors":"Qu Shengyue, Meng Cai","doi":"10.1109/GCIS.2013.47","DOIUrl":null,"url":null,"abstract":"A fast method based on binocular vision is proposed for mobile robot to detect drivable regions. First, the image is segmented into regions by searching contours. Second, part obstacle regions are determined by the vanishing line. Then, according to the different distribution of feature points extracted from the regions under the vanishing line, we use two different method to classify regions: various constraints-based region classification is used to classify regions including many feature points and homography-substraction-based region classification is used to classify regions including rare feature points. Finally, combining the two classification methods, we get the result of drivable region detection. The results of indoor and outdoor experiments show that the method can detect drivable regions quickly and robustly.","PeriodicalId":366262,"journal":{"name":"2013 Fourth Global Congress on Intelligent Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Binocular Vision Based Drivable Region Fast Detection for Indoor Mobile Robot\",\"authors\":\"Qu Shengyue, Meng Cai\",\"doi\":\"10.1109/GCIS.2013.47\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A fast method based on binocular vision is proposed for mobile robot to detect drivable regions. First, the image is segmented into regions by searching contours. Second, part obstacle regions are determined by the vanishing line. Then, according to the different distribution of feature points extracted from the regions under the vanishing line, we use two different method to classify regions: various constraints-based region classification is used to classify regions including many feature points and homography-substraction-based region classification is used to classify regions including rare feature points. Finally, combining the two classification methods, we get the result of drivable region detection. The results of indoor and outdoor experiments show that the method can detect drivable regions quickly and robustly.\",\"PeriodicalId\":366262,\"journal\":{\"name\":\"2013 Fourth Global Congress on Intelligent Systems\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Fourth Global Congress on Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCIS.2013.47\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth Global Congress on Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCIS.2013.47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Binocular Vision Based Drivable Region Fast Detection for Indoor Mobile Robot
A fast method based on binocular vision is proposed for mobile robot to detect drivable regions. First, the image is segmented into regions by searching contours. Second, part obstacle regions are determined by the vanishing line. Then, according to the different distribution of feature points extracted from the regions under the vanishing line, we use two different method to classify regions: various constraints-based region classification is used to classify regions including many feature points and homography-substraction-based region classification is used to classify regions including rare feature points. Finally, combining the two classification methods, we get the result of drivable region detection. The results of indoor and outdoor experiments show that the method can detect drivable regions quickly and robustly.