{"id":"https://openalex.org/W2991472473","doi":"https://doi.org/10.1177/0278364920937608","title":"Exactly sparse Gaussian variational inference with application to derivative-free batch nonlinear state estimation","display_name":"Exactly sparse Gaussian variational inference with application to derivative-free batch nonlinear state estimation","publication_year":2020,"publication_date":"2020-07-29","ids":{"openalex":"https://openalex.org/W2991472473","doi":"https://doi.org/10.1177/0278364920937608","mag":"2991472473"},"language":"en","primary_location":{"id":"doi:10.1177/0278364920937608","is_oa":false,"landing_page_url":"https://doi.org/10.1177/0278364920937608","pdf_url":null,"source":{"id":"https://openalex.org/S73484101","display_name":"The International Journal of Robotics Research","issn_l":"0278-3649","issn":["0278-3649","1741-3176"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International Journal of Robotics Research","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1911.08333","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004788089","display_name":"Timothy D. Barfoot","orcid":"https://orcid.org/0000-0003-3899-631X"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]},{"id":"https://openalex.org/I3143791293","display_name":"Institute for Christian Studies","ror":"https://ror.org/054dn2d35","country_code":"CA","type":"education","lineage":["https://openalex.org/I3143791293"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Timothy D Barfoot","raw_affiliation_strings":["Institute for Aerospace Studies, University of Toronto, Canada","Institute for Aerospace Studies, University of Toronto, Canada#TAB#"],"affiliations":[{"raw_affiliation_string":"Institute for Aerospace Studies, University of Toronto, Canada","institution_ids":["https://openalex.org/I3143791293","https://openalex.org/I185261750"]},{"raw_affiliation_string":"Institute for Aerospace Studies, University of Toronto, Canada#TAB#","institution_ids":["https://openalex.org/I3143791293","https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023450612","display_name":"James Richard Forbes","orcid":"https://orcid.org/0000-0002-1987-9268"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"James R Forbes","raw_affiliation_strings":["Department of Mechanical Engineering, McGill University, Canada","Department of Mechanical Engineering, McGill University, Canada,"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, McGill University, Canada","institution_ids":["https://openalex.org/I5023651"]},{"raw_affiliation_string":"Department of Mechanical Engineering, McGill University, Canada,","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027301346","display_name":"David J. Yoon","orcid":"https://orcid.org/0000-0002-4838-7422"},"institutions":[{"id":"https://openalex.org/I3143791293","display_name":"Institute for Christian Studies","ror":"https://ror.org/054dn2d35","country_code":"CA","type":"education","lineage":["https://openalex.org/I3143791293"]},{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"David J Yoon","raw_affiliation_strings":["Institute for Aerospace Studies, University of Toronto, Canada","Institute for Aerospace Studies, University of Toronto, Canada#TAB#"],"affiliations":[{"raw_affiliation_string":"Institute for Aerospace Studies, University of Toronto, Canada","institution_ids":["https://openalex.org/I3143791293","https://openalex.org/I185261750"]},{"raw_affiliation_string":"Institute for Aerospace Studies, University of Toronto, Canada#TAB#","institution_ids":["https://openalex.org/I3143791293","https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004788089"],"corresponding_institution_ids":["https://openalex.org/I185261750","https://openalex.org/I3143791293"],"apc_list":null,"apc_paid":null,"fwci":0.5438,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72323052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"39","issue":"13","first_page":"1473","last_page":"1502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9997000098228455,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9993000030517578,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/covariance","display_name":"Covariance","score":0.6642298698425293},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5629753470420837},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.558846652507782},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5556839108467102},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5219990015029907},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5109438300132751},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5091540217399597},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.487356036901474},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.48163193464279175},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42612358927726746},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.408467561006546},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.326072096824646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14260461926460266},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11320766806602478}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6642298698425293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5629753470420837},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.558846652507782},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5556839108467102},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5219990015029907},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5109438300132751},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5091540217399597},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.487356036901474},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.48163193464279175},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42612358927726746},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.408467561006546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.326072096824646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14260461926460266},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11320766806602478},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"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":4,"locations":[{"id":"doi:10.1177/0278364920937608","is_oa":false,"landing_page_url":"https://doi.org/10.1177/0278364920937608","pdf_url":null,"source":{"id":"https://openalex.org/S73484101","display_name":"The International Journal of Robotics Research","issn_l":"0278-3649","issn":["0278-3649","1741-3176"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International Journal of Robotics Research","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1911.08333","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.08333","pdf_url":"https://arxiv.org/pdf/1911.08333","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":null,"raw_type":"text"},{"id":"mag:2991472473","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1911.08333","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":"doi:10.48550/arxiv.1911.08333","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1911.08333","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:1911.08333","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.08333","pdf_url":"https://arxiv.org/pdf/1911.08333","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2991472473.pdf","grobid_xml":"https://content.openalex.org/works/W2991472473.grobid-xml"},"referenced_works_count":72,"referenced_works":["https://openalex.org/W88520345","https://openalex.org/W92914038","https://openalex.org/W1539091696","https://openalex.org/W1557554253","https://openalex.org/W1569820002","https://openalex.org/W1610391169","https://openalex.org/W1656165940","https://openalex.org/W1663973292","https://openalex.org/W1746819321","https://openalex.org/W1808206046","https://openalex.org/W1827716646","https://openalex.org/W1849661296","https://openalex.org/W1965555277","https://openalex.org/W1970789124","https://openalex.org/W1974481980","https://openalex.org/W1977464512","https://openalex.org/W1982322007","https://openalex.org/W1990086645","https://openalex.org/W2003967703","https://openalex.org/W2010315317","https://openalex.org/W2024908906","https://openalex.org/W2031086754","https://openalex.org/W2036084078","https://openalex.org/W2036542582","https://openalex.org/W2048305092","https://openalex.org/W2050551672","https://openalex.org/W2054091988","https://openalex.org/W2054640142","https://openalex.org/W2056815852","https://openalex.org/W2065205300","https://openalex.org/W2084840427","https://openalex.org/W2085261163","https://openalex.org/W2089688558","https://openalex.org/W2090963365","https://openalex.org/W2094227853","https://openalex.org/W2099371695","https://openalex.org/W2105934661","https://openalex.org/W2110720940","https://openalex.org/W2115126368","https://openalex.org/W2118429507","https://openalex.org/W2122512809","https://openalex.org/W2124313187","https://openalex.org/W2137052305","https://openalex.org/W2146332636","https://openalex.org/W2146881125","https://openalex.org/W2154319942","https://openalex.org/W2154459632","https://openalex.org/W2160346753","https://openalex.org/W2166851633","https://openalex.org/W2295255295","https://openalex.org/W2336416123","https://openalex.org/W2412669390","https://openalex.org/W2478091498","https://openalex.org/W2478114083","https://openalex.org/W2519820348","https://openalex.org/W2567948266","https://openalex.org/W2736887377","https://openalex.org/W2745007981","https://openalex.org/W2766821096","https://openalex.org/W2798909945","https://openalex.org/W2912813311","https://openalex.org/W2946265133","https://openalex.org/W2963429663","https://openalex.org/W2964202371","https://openalex.org/W2982534705","https://openalex.org/W3003789643","https://openalex.org/W3004157836","https://openalex.org/W3019769413","https://openalex.org/W3083113686","https://openalex.org/W4232464081","https://openalex.org/W4255867308","https://openalex.org/W4302564868"],"related_works":["https://openalex.org/W2125145210","https://openalex.org/W2517464434","https://openalex.org/W2467085974","https://openalex.org/W3011032622","https://openalex.org/W2773200628","https://openalex.org/W164975227","https://openalex.org/W2975231158","https://openalex.org/W2013801751","https://openalex.org/W2859406503","https://openalex.org/W3084552781","https://openalex.org/W2091922944","https://openalex.org/W2423822072","https://openalex.org/W2259656359","https://openalex.org/W2067157583","https://openalex.org/W2671679770","https://openalex.org/W2040969041","https://openalex.org/W2952140635","https://openalex.org/W2055602229","https://openalex.org/W2594140664","https://openalex.org/W86543131"],"abstract_inverted_index":{"We":[0,108,213],"present":[1],"a":[2,35,64,222],"Gaussian":[3,36,87,162,204],"variational":[4,88],"inference":[5,89],"(GVI)":[6],"technique":[7,91,216],"that":[8,76,110,119],"can":[9,155,164],"be":[10,165],"applied":[11],"to":[12,23,26,37,126,139,176],"large-scale":[13],"nonlinear":[14,197,224],"batch":[15,172,184,223],"state":[16,50,75,106],"estimation":[17,186],"problems.":[18,58],"The":[19,83],"main":[20],"contribution":[21],"is":[22,54,60,98],"show":[24,109,137],"how":[25,138],"fit":[27],"both":[28],"the":[29,38,45,49,70,74,78,81,93,111,115,123,127,131,145,178,183,191,196,202,209,215],"mean":[30],"and":[31,51,135,152,206,221,227],"(inverse)":[32],"covariance":[33,95,117,133],"of":[34,44,48,114,130],"posterior":[39,79],"efficiently,":[40],"by":[41],"exploiting":[42],"factorization":[43],"joint":[46],"likelihood":[47],"data,":[52],"as":[53],"common":[55],"in":[56,144,182,195],"practical":[57],"This":[59],"different":[61],"than":[62],"maximum":[63],"posteriori":[65],"(MAP)":[66],"estimation,":[67],"which":[68,97],"seeks":[69],"point":[71,211],"estimate":[72],"for":[73,104],"maximizes":[77],"(i.e.,":[80],"mode).":[82],"proposed":[84],"exactly":[85],"sparse":[86,101],"(ESGVI)":[90],"stores":[92],"inverse":[94,132],"matrix,":[96,134],"typically":[99],"very":[100],"(e.g.,":[102],"block-tridiagonal":[103],"classic":[105],"estimation).":[107],"only":[112],"blocks":[113,129,142],"(dense)":[116],"matrix":[118],"are":[120],"required":[121],"during":[122],"calculations":[124],"correspond":[125],"non-zero":[128],"further":[136],"calculate":[140],"these":[141],"efficiently":[143],"general":[146],"GVI":[147],"problem.":[148],"ESGVI":[149,174],"operates":[150],"iteratively,":[151],"while":[153],"we":[154],"use":[156],"analytical":[157],"derivatives":[158],"at":[159],"each":[160],"iteration,":[161],"cubature":[163],"substituted,":[166],"thereby":[167],"producing":[168],"an":[169,231],"efficient":[170],"derivative-free":[171],"formulation.":[173],"simplifies":[175],"precisely":[177],"Rauch\u2013Tung\u2013Striebel":[179],"(RTS)":[180],"smoother":[181,194],"linear":[185],"case,":[187],"but":[188],"goes":[189],"beyond":[190],"\u2018extended\u2019":[192],"RTS":[193],"case":[198],"because":[199],"it":[200],"finds":[201],"best-fit":[203],"(mean":[205],"covariance),":[207],"not":[208],"MAP":[210],"estimate.":[212],"demonstrate":[214],"on":[217],"controlled":[218],"simulation":[219],"problems":[220],"simultaneous":[225],"localization":[226],"mapping":[228],"problem":[229],"with":[230],"experimental":[232],"dataset.":[233]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2022-07-26T00:00:00"}
