{"title":"AUV航路跟踪的实时侧扫图像生成与配准框架","authors":"P. King, A. Vardy, P. Vandrish, B. Anstey","doi":"10.1109/AUV.2012.6380758","DOIUrl":null,"url":null,"abstract":"Memorial University is in the development stages of a Qualitative Navigation System (QNS) to be deployed on the Memorial Explorer AUV. This system will allow localization and path following along a trained route without the necessity of a globally referenced position estimate. Previous QNS work has been on terrestrial robots using optical images. Our main challenge lies in utilization of side scan sonar as the imaging medium, as this type of sonar is prevalent on AUVs and provides much better range and coverage than optics in water. To achieve this, a sonar image processing and registration framework has been developed. To be useful such a framework should be fully-autonomous, robust, and operate in real-time, where real-time operation is defined as the ability to process, register and localize data at the rate it is collected, or faster. In this paper we describe our framework for processing sonar data, generating image tiles, extracting unique features and localizing against a reference set. We also present some results of this system based on raw sonar input data collected by the AUV.","PeriodicalId":340133,"journal":{"name":"2012 IEEE/OES Autonomous Underwater Vehicles (AUV)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Real-time side scan image generation and registration framework for AUV route following\",\"authors\":\"P. King, A. Vardy, P. Vandrish, B. Anstey\",\"doi\":\"10.1109/AUV.2012.6380758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Memorial University is in the development stages of a Qualitative Navigation System (QNS) to be deployed on the Memorial Explorer AUV. This system will allow localization and path following along a trained route without the necessity of a globally referenced position estimate. Previous QNS work has been on terrestrial robots using optical images. Our main challenge lies in utilization of side scan sonar as the imaging medium, as this type of sonar is prevalent on AUVs and provides much better range and coverage than optics in water. To achieve this, a sonar image processing and registration framework has been developed. To be useful such a framework should be fully-autonomous, robust, and operate in real-time, where real-time operation is defined as the ability to process, register and localize data at the rate it is collected, or faster. In this paper we describe our framework for processing sonar data, generating image tiles, extracting unique features and localizing against a reference set. We also present some results of this system based on raw sonar input data collected by the AUV.\",\"PeriodicalId\":340133,\"journal\":{\"name\":\"2012 IEEE/OES Autonomous Underwater Vehicles (AUV)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE/OES Autonomous Underwater Vehicles (AUV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AUV.2012.6380758\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE/OES Autonomous Underwater Vehicles (AUV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AUV.2012.6380758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time side scan image generation and registration framework for AUV route following
Memorial University is in the development stages of a Qualitative Navigation System (QNS) to be deployed on the Memorial Explorer AUV. This system will allow localization and path following along a trained route without the necessity of a globally referenced position estimate. Previous QNS work has been on terrestrial robots using optical images. Our main challenge lies in utilization of side scan sonar as the imaging medium, as this type of sonar is prevalent on AUVs and provides much better range and coverage than optics in water. To achieve this, a sonar image processing and registration framework has been developed. To be useful such a framework should be fully-autonomous, robust, and operate in real-time, where real-time operation is defined as the ability to process, register and localize data at the rate it is collected, or faster. In this paper we describe our framework for processing sonar data, generating image tiles, extracting unique features and localizing against a reference set. We also present some results of this system based on raw sonar input data collected by the AUV.