Object recognition and localization: the role of tactile sensors.
Identifieur interne : 000740 ( PubMed/Corpus ); précédent : 000739; suivant : 000741Object recognition and localization: the role of tactile sensors.
Auteurs : Achint Aggarwal ; Frank KirchnerSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2014.
Abstract
Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.
DOI: 10.3390/s140203227
PubMed: 24553087
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This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="PubMed-not-MEDLINE"><PMID Version="1">24553087</PMID><DateCreated><Year>2014</Year><Month>02</Month><Day>20</Day></DateCreated><DateCompleted><Year>2014</Year><Month>07</Month><Day>01</Day></DateCompleted><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1424-8220</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>2</Issue><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>Sensors (Basel, Switzerland)</Title><ISOAbbreviation>Sensors (Basel)</ISOAbbreviation></Journal><ArticleTitle>Object recognition and localization: the role of tactile sensors.</ArticleTitle><Pagination><MedlinePgn>3227-66</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3390/s140203227</ELocationID><Abstract><AbstractText>Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Aggarwal</LastName><ForeName>Achint</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, Bremen D-28359, Germany. achint.aggarwal@dfki.de.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kirchner</LastName><ForeName>Frank</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>DFKI GmbH, Robotics Innovation Center (RIC), Robert-Hooke-Str. 1, Bremen D-28359, Germany. frank.kirchner@dfki.de.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>02</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Sensors (Basel)</MedlineTA><NlmUniqueID>101204366</NlmUniqueID><ISSNLinking>1424-8220</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Psychol Rev. 1987 Apr;94(2):115-47</RefSource><PMID Version="1">3575582</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Acta Psychol (Amst). 1993 Oct;84(1):29-40</RefSource><PMID Version="1">8237454</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Cogn Psychol. 1990 Oct;22(4):421-59</RefSource><PMID Version="1">2253454</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Cogn Psychol. 1987 Jul;19(3):342-68</RefSource><PMID Version="1">3608405</PMID></CommentsCorrections></CommentsCorrectionsList><OtherID Source="NLM">PMC3958302</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>12</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>1</Month><Day>31</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>2</Month><Day>08</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>2</Month><Day>21</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>2</Month><Day>21</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>2</Month><Day>21</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pii">s140203227</ArticleId><ArticleId IdType="doi">10.3390/s140203227</ArticleId><ArticleId IdType="pubmed">24553087</ArticleId><ArticleId IdType="pmc">PMC3958302</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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