Speeding up probabilistic roadmap planners with locality-sensitive hashing
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SUMMARYA crucial part of probabilistic roadmap planners is the nearest neighbor search, which is typically done by exact methods. Unfortunately, searching the neighbors can become a major bottleneck for the performance. This can occur when the roadmap size grows especially in high-dimensional spaces. In this paper, we investigate how well the approximate nearest neighbor searching works with probabilistic roadmap planners. We propose a method that is based on the locality-sensitive hashing and show that it can speed up the construction of the roadmap considerably without reducing the quality of the produced roadmap.
2020 ◽
Vol 34
(04)
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pp. 4594-4601
2016 ◽
Vol 6
(3)
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pp. 945
2018 ◽
2018 ◽
2016 ◽
Vol 6
(3)
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pp. 945
2012 ◽
Vol 12
(4)
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pp. 300-307
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2013 ◽
Vol 69
(21)
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pp. 22-31
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