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- W2117496508 abstract "We introduce a fast Nearest Neighbor Search (NNS) algorithm using an Approximate Cached k-d tree (ACk-d tree) structure for low dimensional data sets. The search process of the standard k-d tree starts from the root node and employs a tentative back-tracking search. In contrast, the proposed method begins to search at the appropriate leaf node (cached node) and applies a depth-first nontentative search. This method improves searching speed, with tradeoff of the searching accuracy. To get a proper starting node, the proposed method is based on two properties: i) The i <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>th</sub> query point is likely to be close to the (i-1) <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>th</sub> query point, ii) The i <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>th</sub> query point is likely to be close to the i <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>th</sub> model point. These properties are rather right, in case of practical 3D point sets which are consecutively acquired from 3D point sensors (e.g. a stereo camera, the Kinect sensor, and LIDAR). Results show that the search time of the proposed method is superior to other variants of k-d tree for practical point data sets." @default.
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- W2117496508 date "2012-10-01" @default.
- W2117496508 modified "2023-09-23" @default.
- W2117496508 title "Fast Nearest Neighbor Search using Approximate Cached k-d tree" @default.
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- W2117496508 doi "https://doi.org/10.1109/iros.2012.6385837" @default.
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