{"id":"https://openalex.org/W4378501036","doi":"https://doi.org/10.48550/arxiv.2305.15856","title":"Enhanced 6D Pose Estimation for Robotic Fruit Picking","display_name":"Enhanced 6D Pose Estimation for Robotic Fruit Picking","publication_year":2023,"publication_date":"2023-05-25","ids":{"openalex":"https://openalex.org/W4378501036","doi":"https://doi.org/10.48550/arxiv.2305.15856"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.15856","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.15856","pdf_url":"https://arxiv.org/pdf/2305.15856","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.15856","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031786838","display_name":"Marco Costanzo","orcid":"https://orcid.org/0000-0001-8364-6728"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Costanzo, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102516941","display_name":"Marco De Simone","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De Simone, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046612625","display_name":"Sara M. Federico","orcid":"https://orcid.org/0000-0002-5807-4876"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Federico, Sara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041446451","display_name":"Ciro Natale","orcid":"https://orcid.org/0000-0001-6550-0573"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Natale, Ciro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016424724","display_name":"Salvatore Pirozzi","orcid":"https://orcid.org/0000-0002-1237-0389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pirozzi, Salvatore","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10868","display_name":"Soft Robotics and Applications","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.8658075332641602},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.7790645360946655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7720514535903931},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6827503442764282},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.681713342666626},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6407326459884644},{"id":"https://openalex.org/keywords/slippage","display_name":"Slippage","score":0.5980623364448547},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5471025705337524},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5390419960021973},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.529126763343811},{"id":"https://openalex.org/keywords/lift","display_name":"Lift (data mining)","score":0.5168700218200684},{"id":"https://openalex.org/keywords/cad","display_name":"CAD","score":0.513064980506897},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5091792941093445},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.48066437244415283},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20708629488945007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13252055644989014},{"id":"https://openalex.org/keywords/engineering-drawing","display_name":"Engineering drawing","score":0.11210399866104126}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.8658075332641602},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7790645360946655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7720514535903931},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6827503442764282},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.681713342666626},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6407326459884644},{"id":"https://openalex.org/C2776096238","wikidata":"https://www.wikidata.org/wiki/Q6130172","display_name":"Slippage","level":2,"score":0.5980623364448547},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5471025705337524},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5390419960021973},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.529126763343811},{"id":"https://openalex.org/C139002025","wikidata":"https://www.wikidata.org/wiki/Q3001212","display_name":"Lift (data mining)","level":2,"score":0.5168700218200684},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.513064980506897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5091792941093445},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.48066437244415283},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20708629488945007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13252055644989014},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.11210399866104126},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.15856","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.15856","pdf_url":"https://arxiv.org/pdf/2305.15856","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2305.15856","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.15856","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.15856","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.15856","pdf_url":"https://arxiv.org/pdf/2305.15856","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378501036.pdf","grobid_xml":"https://content.openalex.org/works/W4378501036.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2727798326","https://openalex.org/W3021551196","https://openalex.org/W4253893311","https://openalex.org/W2113785214","https://openalex.org/W2798721181","https://openalex.org/W3201205132","https://openalex.org/W4287600488","https://openalex.org/W4312694060","https://openalex.org/W4386075737","https://openalex.org/W4387967917"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,21,44,63,82,133],"novel":[4],"method":[5],"to":[6,48,84,113,149,158],"refine":[7],"the":[8,40,50,53,58,68,78,100,107,116,121,154,160,169],"6D":[9],"pose":[10,79,102],"estimation":[11,80,103],"inferred":[12],"by":[13],"an":[14],"instance-level":[15],"deep":[16],"neural":[17],"network":[18,59],"which":[19],"processes":[20],"single":[22,64],"RGB":[23],"image":[24],"and":[25,90,165],"that":[26],"has":[27],"been":[28],"trained":[29,61],"on":[30,62],"synthetic":[31],"images":[32],"only.":[33],"The":[34,74],"proposed":[35],"optimization":[36],"algorithm":[37],"usefully":[38],"exploits":[39],"depth":[41],"measurement":[42],"of":[43,52,67,87,115,175],"standard":[45,101],"RGB-D":[46],"camera":[47],"estimate":[49],"dimensions":[51,93,110],"considered":[54],"object,":[55],"even":[56],"though":[57],"is":[60,146],"CAD":[65,117],"model":[66],"same":[69,170],"object":[70],"with":[71,109,142,153,172],"given":[72],"dimensions.":[73],"improved":[75],"accuracy":[76],"in":[77,120],"allows":[81],"robot":[83],"grasp":[85,135],"apples":[86],"various":[88],"types":[89],"significantly":[91],"different":[92],"successfully;":[94],"this":[95],"was":[96],"not":[97],"possible":[98],"using":[99],"algorithm,":[104],"except":[105],"for":[106],"fruits":[108,126,161],"very":[111],"close":[112],"those":[114],"drawing":[118],"used":[119],"training":[122],"process.":[123],"Grasping":[124],"fresh":[125],"without":[127,162],"damaging":[128],"each":[129],"item":[130],"also":[131],"demands":[132],"suitable":[134],"force":[136,156],"control.":[137],"A":[138],"parallel":[139],"gripper":[140],"equipped":[141],"special":[143],"force/tactile":[144],"sensors":[145],"thus":[147],"adopted":[148],"achieve":[150],"safe":[151],"grasps":[152],"minimum":[155],"necessary":[157],"lift":[159],"any":[163,166],"slippage":[164],"deformation":[167],"at":[168],"time,":[171],"no":[173],"knowledge":[174],"their":[176],"weight.":[177]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}