{"id":"https://openalex.org/W3046286154","doi":"https://doi.org/10.1109/iros45743.2020.9341404","title":"Variational Filtering with Copula Models for SLAM","display_name":"Variational Filtering with Copula Models for SLAM","publication_year":2020,"publication_date":"2020-10-24","ids":{"openalex":"https://openalex.org/W3046286154","doi":"https://doi.org/10.1109/iros45743.2020.9341404","mag":"3046286154"},"language":"en","primary_location":{"id":"doi:10.1109/iros45743.2020.9341404","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.00504","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102806974","display_name":"John D. Martin","orcid":"https://orcid.org/0000-0002-1264-2786"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"John D. Martin","raw_affiliation_strings":["Stevens Institute of Technology","STEVENS INST OF TECHNOLOGY"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology","institution_ids":["https://openalex.org/I108468826"]},{"raw_affiliation_string":"STEVENS INST OF TECHNOLOGY","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012181272","display_name":"Kevin Doherty","orcid":"https://orcid.org/0000-0002-8924-2502"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Doherty","raw_affiliation_strings":["Massachusetts Institute of Technology","Massachusetts Institute Of Technology#TAB#"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]},{"raw_affiliation_string":"Massachusetts Institute Of Technology#TAB#","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059953027","display_name":"Caralyn Cyr","orcid":null},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Caralyn Cyr","raw_affiliation_strings":["Stevens Institute of Technology","STEVENS INST OF TECHNOLOGY"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology","institution_ids":["https://openalex.org/I108468826"]},{"raw_affiliation_string":"STEVENS INST OF TECHNOLOGY","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044251670","display_name":"Brendan Englot","orcid":"https://orcid.org/0000-0002-7966-2917"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brendan Englot","raw_affiliation_strings":["Stevens Institute of Technology","STEVENS INST OF TECHNOLOGY"],"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology","institution_ids":["https://openalex.org/I108468826"]},{"raw_affiliation_string":"STEVENS INST OF TECHNOLOGY","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110293164","display_name":"John J. Leonard","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Leonard","raw_affiliation_strings":["Massachusetts Institute of Technology","Massachusetts Institute Of Technology#TAB#"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology","institution_ids":["https://openalex.org/I63966007"]},{"raw_affiliation_string":"Massachusetts Institute Of Technology#TAB#","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102806974"],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":null,"apc_paid":null,"fwci":3.2561,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.94932981,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":95},"biblio":{"volume":"25","issue":null,"first_page":"5066","last_page":"5073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/copula","display_name":"Copula (linguistics)","score":0.6935902833938599},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.6054530143737793},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5799680948257446},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5767223834991455},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.57538902759552},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.540600597858429},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4981555938720703},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.41867348551750183},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4173000156879425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40459635853767395},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3729879558086395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3467467129230499},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.31985974311828613},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18987584114074707},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15990018844604492}],"concepts":[{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.6935902833938599},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.6054530143737793},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5799680948257446},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5767223834991455},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.57538902759552},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.540600597858429},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4981555938720703},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.41867348551750183},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4173000156879425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40459635853767395},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3729879558086395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3467467129230499},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.31985974311828613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18987584114074707},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15990018844604492},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/iros45743.2020.9341404","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2008.00504","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.00504","pdf_url":"https://arxiv.org/pdf/2008.00504","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"mag:3046286154","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2008.00504","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:dspace.mit.edu:1721.1/138849","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/138849","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv","raw_type":"http://purl.org/eprint/type/ConferencePaper"},{"id":"pmh:oai:dspace.mit.edu:1721.1/138849.2","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/138849.2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv","raw_type":"http://purl.org/eprint/type/ConferencePaper"},{"id":"doi:10.48550/arxiv.2008.00504","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2008.00504","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2008.00504","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.00504","pdf_url":"https://arxiv.org/pdf/2008.00504","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"id":"https://openalex.org/G664400679","display_name":"CAREER: Belief Space Planning and Learning for Uncertainty-Immersed Underwater Robots","funder_award_id":"1652064","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3046286154.pdf","grobid_xml":"https://content.openalex.org/works/W3046286154.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W49490181","https://openalex.org/W95577512","https://openalex.org/W299440670","https://openalex.org/W1596676983","https://openalex.org/W1656165940","https://openalex.org/W1804110266","https://openalex.org/W1824523713","https://openalex.org/W1959608418","https://openalex.org/W1968315983","https://openalex.org/W1990884907","https://openalex.org/W1992542543","https://openalex.org/W2023103251","https://openalex.org/W2024908906","https://openalex.org/W2031446601","https://openalex.org/W2045995235","https://openalex.org/W2098613108","https://openalex.org/W2119436275","https://openalex.org/W2130422193","https://openalex.org/W2137052305","https://openalex.org/W2148228392","https://openalex.org/W2148803771","https://openalex.org/W2148820580","https://openalex.org/W2152422184","https://openalex.org/W2160899649","https://openalex.org/W2173782939","https://openalex.org/W2186350170","https://openalex.org/W2336416123","https://openalex.org/W2563938752","https://openalex.org/W2766235398","https://openalex.org/W2771230829","https://openalex.org/W2907233437","https://openalex.org/W2943873223","https://openalex.org/W2954128634","https://openalex.org/W2962837602","https://openalex.org/W2963659509","https://openalex.org/W2963910604","https://openalex.org/W2963936947","https://openalex.org/W2964121744","https://openalex.org/W2964334616","https://openalex.org/W2967044683","https://openalex.org/W3130999122","https://openalex.org/W6610566761","https://openalex.org/W6631190155","https://openalex.org/W6634368179","https://openalex.org/W6638670479","https://openalex.org/W6640963894","https://openalex.org/W6680954251","https://openalex.org/W6681983703","https://openalex.org/W6682324061","https://openalex.org/W6682906688","https://openalex.org/W6728917834","https://openalex.org/W6738609897","https://openalex.org/W6745476292","https://openalex.org/W6746700961"],"related_works":["https://openalex.org/W3213324598","https://openalex.org/W2949481931","https://openalex.org/W2140660104","https://openalex.org/W2758086775","https://openalex.org/W3106106967","https://openalex.org/W2105805319","https://openalex.org/W2949987155","https://openalex.org/W3197425476","https://openalex.org/W2561818583","https://openalex.org/W2949422027","https://openalex.org/W2157404564","https://openalex.org/W2791239056","https://openalex.org/W2344676630","https://openalex.org/W3199183131","https://openalex.org/W2012656342","https://openalex.org/W3161841851","https://openalex.org/W2790067608","https://openalex.org/W2909133069","https://openalex.org/W3032039486","https://openalex.org/W3022044250"],"abstract_inverted_index":{"The":[0],"ability":[1],"to":[2,11,51],"infer":[3],"map":[4],"variables":[5,26],"and":[6,55,59,90,123],"estimate":[7],"pose":[8],"is":[9,27,42,49,71,106],"crucial":[10],"the":[12,21,79],"operation":[13],"of":[14,66],"autonomous":[15],"mobile":[16],"robots.":[17],"In":[18],"most":[19],"cases":[20],"shared":[22],"dependency":[23,70],"between":[24],"these":[25],"modeled":[28],"through":[29,99],"a":[30,63,74,85],"multivariate":[31,69],"Gaussian":[32,111],"distribution,":[33],"but":[34],"there":[35],"are":[36,113],"many":[37],"situations":[38],"where":[39,110],"that":[40],"assumption":[41,54],"unrealistic.":[43],"Our":[44],"paper":[45],"shows":[46],"how":[47,92],"it":[48],"possible":[50],"relax":[52],"this":[53],"perform":[56],"simultaneous":[57],"localization":[58],"mapping":[60],"(SLAM)":[61],"with":[62,73,82,119],"larger":[64],"class":[65],"distributions,":[67],"whose":[68],"represented":[72],"copula":[75],"model.":[76],"We":[77,102],"integrate":[78],"distribution":[80],"model":[81,94],"copulas":[83],"into":[84],"Sequential":[86],"Monte":[87],"Carlo":[88],"estimator":[89],"show":[91],"unknown":[93],"parameters":[95],"can":[96],"be":[97],"learned":[98],"gradient-based":[100],"optimization.":[101],"demonstrate":[103],"our":[104],"approach":[105],"effective":[107],"in":[108],"settings":[109],"assumptions":[112],"clearly":[114],"violated,":[115],"such":[116],"as":[117],"environments":[118],"uncertain":[120],"data":[121],"association":[122],"nonlinear":[124],"transition":[125],"models.":[126]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}