D. Bandara, Z. Leong, H. Nguyen, S. Jayasinghe, A. Forrest
{"title":"Technologies for under-ice AUV navigation","authors":"D. Bandara, Z. Leong, H. Nguyen, S. Jayasinghe, A. Forrest","doi":"10.1109/AUV.2016.7778657","DOIUrl":null,"url":null,"abstract":"Approximately 12% of the world's oceans are covered by ice. Understanding the physical processes, ecosystem structure, mixing dynamics and the role of these inaccessible environments in the context of global climate change is extremely important. Autonomous Underwater Vehicles (AUVs) play a major role in the potential exploration of these water systems due to the challenges of human access and relatively high associated risk. That said, AUV navigation and localization is challenging in these environments due to the unavoidable growth of navigational drift associated with inertial navigation systems, especially in long range missions under ice where surfacing in open water is not possible. While acoustic transponders have been used, they are time consuming and difficult to deploy. Terrain Relative Navigation (TRN) and Simultaneous Localization and Mapping (SLAM) based technologies are emerging in recent years as promising navigation solutions as they neither require deploying navigational aids or calculating the distance travelled from a reference point to determine location. One of the key challenges of underwater or under-ice image based localization results from the unstructured nature and lack of significant features in underwater environments. This issue has motivated the review presented in this paper, which outlines a potential area of under-ice AUV navigation and localization by combining TRN and SLAM with image matching methods for navigation in featureless environments.","PeriodicalId":416057,"journal":{"name":"2016 IEEE/OES Autonomous Underwater Vehicles (AUV)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE/OES Autonomous Underwater Vehicles (AUV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AUV.2016.7778657","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Approximately 12% of the world's oceans are covered by ice. Understanding the physical processes, ecosystem structure, mixing dynamics and the role of these inaccessible environments in the context of global climate change is extremely important. Autonomous Underwater Vehicles (AUVs) play a major role in the potential exploration of these water systems due to the challenges of human access and relatively high associated risk. That said, AUV navigation and localization is challenging in these environments due to the unavoidable growth of navigational drift associated with inertial navigation systems, especially in long range missions under ice where surfacing in open water is not possible. While acoustic transponders have been used, they are time consuming and difficult to deploy. Terrain Relative Navigation (TRN) and Simultaneous Localization and Mapping (SLAM) based technologies are emerging in recent years as promising navigation solutions as they neither require deploying navigational aids or calculating the distance travelled from a reference point to determine location. One of the key challenges of underwater or under-ice image based localization results from the unstructured nature and lack of significant features in underwater environments. This issue has motivated the review presented in this paper, which outlines a potential area of under-ice AUV navigation and localization by combining TRN and SLAM with image matching methods for navigation in featureless environments.