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- Publisher Website: 10.1007/978-3-319-29363-9_5
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Conference Paper: Probabilistic collision detection between noisy point clouds using robust classification
Title | Probabilistic collision detection between noisy point clouds using robust classification |
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Authors | |
Issue Date | 2017 |
Publisher | Springer. |
Citation | 15th International Symposium on Robotics Research, Flagstaff, Arizona, 28 August-1 September 2011. In Christensen, HI, Khatib, O (Eds.), Robotics Research: The 15th International Symposium ISRR, p. 77-94. Cham: Springer, 2017 How to Cite? |
Abstract | We present a new collision detection algorithm to perform contact computations between noisy point cloud data. Our approach takes into account the uncertainty that arises due to discretization error and noise, and formulates collision checking as a two-class classification problem. We use techniques from machine learning to compute the collision probability for each point in the input data and accelerate the computation using stochastic traversal of bounding volume hierarchies. We highlight the performance of our algorithm on point clouds captured using PR2 sensors as well as synthetic data sets, and show that our approach can provide a fast and robust solution for handling uncertainty in contact computations. |
Persistent Identifier | http://hdl.handle.net/10722/308920 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.485 |
ISI Accession Number ID | |
Series/Report no. | Springer Tracts in Advanced Robotics ; 100 |
DC Field | Value | Language |
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dc.contributor.author | Pan, Jia | - |
dc.contributor.author | Chitta, Sachin | - |
dc.contributor.author | Manocha, Dinesh | - |
dc.date.accessioned | 2021-12-08T07:50:24Z | - |
dc.date.available | 2021-12-08T07:50:24Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 15th International Symposium on Robotics Research, Flagstaff, Arizona, 28 August-1 September 2011. In Christensen, HI, Khatib, O (Eds.), Robotics Research: The 15th International Symposium ISRR, p. 77-94. Cham: Springer, 2017 | - |
dc.identifier.isbn | 9783319293622 | - |
dc.identifier.issn | 1610-7438 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308920 | - |
dc.description.abstract | We present a new collision detection algorithm to perform contact computations between noisy point cloud data. Our approach takes into account the uncertainty that arises due to discretization error and noise, and formulates collision checking as a two-class classification problem. We use techniques from machine learning to compute the collision probability for each point in the input data and accelerate the computation using stochastic traversal of bounding volume hierarchies. We highlight the performance of our algorithm on point clouds captured using PR2 sensors as well as synthetic data sets, and show that our approach can provide a fast and robust solution for handling uncertainty in contact computations. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Robotics Research: The 15th International Symposium ISRR | - |
dc.relation.ispartofseries | Springer Tracts in Advanced Robotics ; 100 | - |
dc.title | Probabilistic collision detection between noisy point clouds using robust classification | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-319-29363-9_5 | - |
dc.identifier.scopus | eid_2-s2.0-84984853843 | - |
dc.identifier.spage | 77 | - |
dc.identifier.epage | 94 | - |
dc.identifier.eissn | 1610-742X | - |
dc.identifier.isi | WOS:000405326800005 | - |
dc.publisher.place | Cham | - |