{"title":"基于图像语义分割的静态三维地图重建","authors":"Feiran Li, Ming Ding, J. Takamatsu, T. Ogasawara","doi":"10.1109/URAI.2018.8441765","DOIUrl":null,"url":null,"abstract":"In this paper we propose a novel approach to remove dynamic objects from the reconstructed 3D map. We use a pixel-wise, fully convolutional neural network (FCN) based image semantic segmentation method to detect the moving objects and coherently remove them. Both 2D and 3D filters are employed to deal with the stubborn unwanted pixels to preserve as much information as possible. Experiment shows our approach can effectively build static maps.","PeriodicalId":347727,"journal":{"name":"2018 15th International Conference on Ubiquitous Robots (UR)","volume":"6 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Static 3D Map Reconstruction based on Image Semantic Segmentation\",\"authors\":\"Feiran Li, Ming Ding, J. Takamatsu, T. Ogasawara\",\"doi\":\"10.1109/URAI.2018.8441765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a novel approach to remove dynamic objects from the reconstructed 3D map. We use a pixel-wise, fully convolutional neural network (FCN) based image semantic segmentation method to detect the moving objects and coherently remove them. Both 2D and 3D filters are employed to deal with the stubborn unwanted pixels to preserve as much information as possible. Experiment shows our approach can effectively build static maps.\",\"PeriodicalId\":347727,\"journal\":{\"name\":\"2018 15th International Conference on Ubiquitous Robots (UR)\",\"volume\":\"6 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 15th International Conference on Ubiquitous Robots (UR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/URAI.2018.8441765\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th International Conference on Ubiquitous Robots (UR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/URAI.2018.8441765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Static 3D Map Reconstruction based on Image Semantic Segmentation
In this paper we propose a novel approach to remove dynamic objects from the reconstructed 3D map. We use a pixel-wise, fully convolutional neural network (FCN) based image semantic segmentation method to detect the moving objects and coherently remove them. Both 2D and 3D filters are employed to deal with the stubborn unwanted pixels to preserve as much information as possible. Experiment shows our approach can effectively build static maps.
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