생성적 적대 신경망을 이용한 행성의 장거리 2차원 깊이광역 위치 추정 방법
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 아하메드 엠.나기브[아하메드 엠.나기브] | - |
dc.contributor.author | 투안 아인 뉴엔[투안 아인 뉴엔] | - |
dc.contributor.author | 나임 울 이슬람[나임 울 이슬람] | - |
dc.contributor.author | 김재웅[김재웅] | - |
dc.contributor.author | 이석한[이석한] | - |
dc.date.accessioned | 2021-07-29T20:45:20Z | - |
dc.date.available | 2021-07-29T20:45:20Z | - |
dc.date.created | 2020-07-15 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1975-6291 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/22976 | - |
dc.description.abstract | Planetary global localization is necessary for long-range rover missions in which communication with command center operator is throttled due to the long distance. There has been number of researches that address this problem by exploiting and matching rover surroundings with global digital elevation maps (DEM). Using conventional methods for matching, however, is challenging due to artifacts in both DEM rendered images, and/or rover 2D images caused by DEM low resolution, rover image illumination variations and small terrain features. In this work, we use train CNN discriminator to match rover 2D image with DEM rendered images using conditional Generative Adversarial Network architecture (cGAN). We then use this discriminator to search an uncertainty bound given by visual odometry (VO) error bound to estimate rover optimal location and orientation. We demonstrate our network capability to learn to translate rover image into DEM simulated image and match them using Devon Island dataset. The experimental results show that our proposed approach achieves ~74% mean average precision. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국로봇학회 | - |
dc.title | 생성적 적대 신경망을 이용한 행성의 장거리 2차원 깊이광역 위치 추정 방법 | - |
dc.title.alternative | Planetary Long-Range Deep 2D Global Localization Using Generative Adversarial Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 아하메드 엠.나기브[아하메드 엠.나기브] | - |
dc.contributor.affiliatedAuthor | 투안 아인 뉴엔[투안 아인 뉴엔] | - |
dc.contributor.affiliatedAuthor | 나임 울 이슬람[나임 울 이슬람] | - |
dc.contributor.affiliatedAuthor | 김재웅[김재웅] | - |
dc.contributor.affiliatedAuthor | 이석한[이석한] | - |
dc.identifier.doi | 10.7746/jkros.2018.13.1.026 | - |
dc.identifier.bibliographicCitation | 로봇학회 논문지, v.13, no.1, pp.26 - 30 | - |
dc.relation.isPartOf | 로봇학회 논문지 | - |
dc.citation.title | 로봇학회 논문지 | - |
dc.citation.volume | 13 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 26 | - |
dc.citation.endPage | 30 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002318277 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Global Localization System | - |
dc.subject.keywordAuthor | Conditional Generative Adversarial Network | - |
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