{"title":"基于RGBD语义分割的室内地图构建算法","authors":"Jie-Ying He","doi":"10.1145/3548608.3559274","DOIUrl":null,"url":null,"abstract":"In the field of robotics and autonomous driving, vision or laser-based map construction has always been the main direction for solving mobile vehicle's perception and localization. The stereo camera is widely used in robot map construction because it can perceive both color information and depth information. Based on the RGBD semantic segmentation network, this paper proposes a map construction algorithm based on deep semantic segmentation. By using the pixel information of deep semantic segmentation, the missing part of the 3D point cloud is filled to construct an octomap. After experiments, on the datasets, the algorithm has achieved better results than only using the depth information, and after actual deployment, the algorithm has completed the construction of real-time indoor semantic maps on the robot.","PeriodicalId":201434,"journal":{"name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Indoor map construction algorithm based on RGBD semantic segmentation\",\"authors\":\"Jie-Ying He\",\"doi\":\"10.1145/3548608.3559274\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the field of robotics and autonomous driving, vision or laser-based map construction has always been the main direction for solving mobile vehicle's perception and localization. The stereo camera is widely used in robot map construction because it can perceive both color information and depth information. Based on the RGBD semantic segmentation network, this paper proposes a map construction algorithm based on deep semantic segmentation. By using the pixel information of deep semantic segmentation, the missing part of the 3D point cloud is filled to construct an octomap. After experiments, on the datasets, the algorithm has achieved better results than only using the depth information, and after actual deployment, the algorithm has completed the construction of real-time indoor semantic maps on the robot.\",\"PeriodicalId\":201434,\"journal\":{\"name\":\"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3548608.3559274\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3548608.3559274","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Indoor map construction algorithm based on RGBD semantic segmentation
In the field of robotics and autonomous driving, vision or laser-based map construction has always been the main direction for solving mobile vehicle's perception and localization. The stereo camera is widely used in robot map construction because it can perceive both color information and depth information. Based on the RGBD semantic segmentation network, this paper proposes a map construction algorithm based on deep semantic segmentation. By using the pixel information of deep semantic segmentation, the missing part of the 3D point cloud is filled to construct an octomap. After experiments, on the datasets, the algorithm has achieved better results than only using the depth information, and after actual deployment, the algorithm has completed the construction of real-time indoor semantic maps on the robot.