{"title":"机器人移动的结构光模式","authors":"J. L. Moigne, A. Waxman","doi":"10.1109/56.20439","DOIUrl":null,"url":null,"abstract":"The authors describe some of the important operational considerations and image processing tasks required to utilize a nonscanning structured-light range sensor in path planning for robot mobility. The efforts have concentrated on the geometric design of the projectional grid with regard to smoothing of fine-scale range texture, and the development of image processing techniques in order to extract the deformed grid from the image. Particular emphasis is placed on the issues of operating in ambient lighting, smoothing of range texture, grid pattern selection, albedo normalization, grid extraction, and coarse registration of images to the projected grid. Once the grid is extracted, its global order allows intersections to be labeled, and disparities assigned and converted to range data. This range map can then be converted to a 'topography map' to be used for planning short-range paths through the environment while avoiding obstacles. >","PeriodicalId":370047,"journal":{"name":"IEEE J. Robotics Autom.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"67","resultStr":"{\"title\":\"Structured light patterns for robot mobility\",\"authors\":\"J. L. Moigne, A. Waxman\",\"doi\":\"10.1109/56.20439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors describe some of the important operational considerations and image processing tasks required to utilize a nonscanning structured-light range sensor in path planning for robot mobility. The efforts have concentrated on the geometric design of the projectional grid with regard to smoothing of fine-scale range texture, and the development of image processing techniques in order to extract the deformed grid from the image. Particular emphasis is placed on the issues of operating in ambient lighting, smoothing of range texture, grid pattern selection, albedo normalization, grid extraction, and coarse registration of images to the projected grid. Once the grid is extracted, its global order allows intersections to be labeled, and disparities assigned and converted to range data. This range map can then be converted to a 'topography map' to be used for planning short-range paths through the environment while avoiding obstacles. >\",\"PeriodicalId\":370047,\"journal\":{\"name\":\"IEEE J. Robotics Autom.\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"67\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE J. Robotics Autom.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/56.20439\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE J. Robotics Autom.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/56.20439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The authors describe some of the important operational considerations and image processing tasks required to utilize a nonscanning structured-light range sensor in path planning for robot mobility. The efforts have concentrated on the geometric design of the projectional grid with regard to smoothing of fine-scale range texture, and the development of image processing techniques in order to extract the deformed grid from the image. Particular emphasis is placed on the issues of operating in ambient lighting, smoothing of range texture, grid pattern selection, albedo normalization, grid extraction, and coarse registration of images to the projected grid. Once the grid is extracted, its global order allows intersections to be labeled, and disparities assigned and converted to range data. This range map can then be converted to a 'topography map' to be used for planning short-range paths through the environment while avoiding obstacles. >