VLSH: voronoi-based locality sensitive hashingVLSH: 보로노이 기반 국지성 민감 해싱

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dc.contributor.advisorYoon, Sung-Eui-
dc.contributor.advisor윤 성의-
dc.contributor.authorTieu Lin L Loi-
dc.contributor.authorLoi-
dc.date.accessioned2013-09-12T01:48:14Z-
dc.date.available2013-09-12T01:48:14Z-
dc.date.issued2013-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=515176&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/180414-
dc.description학위논문(석사) - 한국과학기술원 : 전산학과, 2013.2, [ iv, 21 p. ]-
dc.description.abstractWe present a fast, yet accurate k-nearest neighbor search algorithm for high-dimensional sampling-based motion planners. Our technique is built on top of Locality Sensitive Hashing (LSH), but is extended to support arbitrary distance metrics for motion planning problems and adapt irregular distributions of samples generated in the configuration space. To enable such novel characteristics our method embeds samples generated in the configuration space into a simple L2 norm space by introducing pivot points. We then implicitly define Voronoi regions an use local LSHs with varying quantization factors for those Voronoi regions. We have applied our method and other prior techniques to high-dimensional motion planning problems. Our method is able to show performance improvement by a factor of up to three times even with higher accuracy over prior, approximate nearest neighbor search techniques.eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectSampling-based motion planner-
dc.subjectLocality Sensitive Hashing-
dc.subject샘플링 기반 모션 플래너-
dc.subject국지성 민감 해싱-
dc.subject근접점 탐색 알고리즘-
dc.subjectNear neighbor search-
dc.titleVLSH: voronoi-based locality sensitive hashing-
dc.title.alternativeVLSH: 보로노이 기반 국지성 민감 해싱-
dc.typeThesis(Master)-
dc.identifier.CNRN515176/325007 -
dc.description.department한국과학기술원 : 전산학과, -
dc.identifier.uid020114577-
dc.contributor.localauthorYoon, Sung-Eui-
dc.contributor.localauthor윤 성의-
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