Consistent local mapping using the invariant extended Kalman filter불변 칼만 필터를 이용한 국부 지도 생성

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 205
  • Download : 0
DC FieldValueLanguage
dc.contributor.advisorKim, Jinwhan-
dc.contributor.advisor김진환-
dc.contributor.authorZino, David Munk-
dc.date.accessioned2021-05-13T19:37:34Z-
dc.date.available2021-05-13T19:37:34Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925115&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/284956-
dc.description학위논문(석사) - 한국과학기술원 : 기계공학과, 2020.8,[iv, 55 p. :]-
dc.description.abstractRecent advancements in non-linear filtering techniques provide for an opportunity to improve and rid past consistency issues in large scale simultaneous localization and mapping based on the extended Kalman filter as the chosen optimization method. This is important for the multitude of industries that rely on such algorithms when the Global Positioning System or other means of extracting absolute measurements of one's whereabouts is not available. Especially in robotics and augmented reality is exact and reliable localization information crucial for operation as well as acquiring the structure of the environment. In this study, the invariant extended Kalman filter is rigorously investigated for its ability to correctly model uncertainty of the estimated states by avoiding the unfortunate mathematical properties of the current state of the art implementations of filter-based simultaneous localization and mapping-
dc.description.abstractthat is, the need to linearize around the true trajectory but as this information is obviously unknown, one must evaluate the linearization around the estimate instead which can lead to inconsistencies. This enables robust and consistent fusing of local maps via the means of the information filter formulation which will save tremendous computational effort and reduce memory requirements. In order to use the non-linear uncertainty presentation for local mapping a novel extension of the framework of local mapping using the extended information filter is presented and tested in simulation and on a real data set using a camera and inertial measurements.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectSimultaneous localization and mapping-
dc.subjectinvariant extended Kalman filter-
dc.subjectextended information filter-
dc.subjectlocal mapping-
dc.subjectmap fusion-
dc.subject동시적 위치추정 및 지도작성-
dc.subject불변 확장칼만필터-
dc.subject확장 정보필터-
dc.subject로컬 매핑-
dc.subject지도 정합-
dc.titleConsistent local mapping using the invariant extended Kalman filter-
dc.title.alternative불변 칼만 필터를 이용한 국부 지도 생성-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :기계공학과,-
dc.contributor.alternativeauthor지노 데이비드 뭉크-
Appears in Collection
ME-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0