DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Woo-Cheol | ko |
dc.contributor.author | Choi, Han-Lim | ko |
dc.date.accessioned | 2022-01-05T06:41:45Z | - |
dc.date.available | 2022-01-05T06:41:45Z | - |
dc.date.created | 2022-01-04 | - |
dc.date.created | 2022-01-04 | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE ACCESS, v.9, pp.167039 - 167053 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/291555 | - |
dc.description.abstract | This paper addresses the problem of target-directed exploration (TDE) in initially unknown and large-scale indoor environments. In such scenarios, the inference on an unknown space can improve the search performance under the assumption that the context of a particular space (i.e., the functional category of the space) is correlated with the existence of a target. The space inference is promising in that there is a strong statistical correlation between the semantic categories of indoor spaces and their adjacency because the spaces are designed to reflect universal human preferences. In this point of view, we propose a novel TDE scheme leveraging the semantic-spatial relations of an indoor floorplan dataset. Whereas existing works dealing with the data-driven space inferences consider only the one-to-one relation statistics of the spaces or utilize heuristic counting-based matching algorithms without building a trainable latent model, we propose the pattern cognitive Multivariate Bernoulli Distribution-based Graphical Space Inference Model (MBD-GSIM). MBD-GSIM efficiently captures the core contexts of the discrete semantic-spatial relations to predict an unknown space by using the latent Multivariate Bernoulli Distribution model. We also suggest utilizing the MBD-GSIM in a cost-utility based frontier exploration scheme for TDE problems. The proposed scheme is constructed in the Robot Operating System (ROS); its efficiency is investigated in the Gazebo simulation environment. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Complex Semantic-Spatial Relation Aided Indoor Target-Directed Exploration | - |
dc.type | Article | - |
dc.identifier.wosid | 000733941400001 | - |
dc.identifier.scopusid | 2-s2.0-85121335622 | - |
dc.type.rims | ART | - |
dc.citation.volume | 9 | - |
dc.citation.beginningpage | 167039 | - |
dc.citation.endingpage | 167053 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3133567 | - |
dc.contributor.localauthor | Choi, Han-Lim | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Robots | - |
dc.subject.keywordAuthor | Semantics | - |
dc.subject.keywordAuthor | Navigation | - |
dc.subject.keywordAuthor | Context modeling | - |
dc.subject.keywordAuthor | Space missions | - |
dc.subject.keywordAuthor | Robot sensing systems | - |
dc.subject.keywordAuthor | Predictive models | - |
dc.subject.keywordAuthor | Indoor exploration | - |
dc.subject.keywordAuthor | robot exploration | - |
dc.subject.keywordAuthor | robotics and automation | - |
dc.subject.keywordAuthor | indoor environments | - |
dc.subject.keywordAuthor | inference algorithms | - |
dc.subject.keywordAuthor | indoor space inference | - |
dc.subject.keywordAuthor | planning | - |
dc.subject.keywordAuthor | computation artificial intelligence | - |
dc.subject.keywordAuthor | semantic-spatial relation | - |
dc.subject.keywordPlus | SEARCH | - |
dc.subject.keywordPlus | OBJECT | - |
dc.subject.keywordPlus | ATTENTION | - |
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