{"id":"https://openalex.org/W3119595865","doi":"https://doi.org/10.1109/lra.2021.3051571","title":"Unsupervised Balanced Covariance Learning for Visual-Inertial Sensor Fusion","display_name":"Unsupervised Balanced Covariance Learning for Visual-Inertial Sensor Fusion","publication_year":2021,"publication_date":"2021-01-14","ids":{"openalex":"https://openalex.org/W3119595865","doi":"https://doi.org/10.1109/lra.2021.3051571","mag":"3119595865"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2021.3051571","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2021.3051571","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101571418","display_name":"Youngji Kim","orcid":"https://orcid.org/0000-0003-0497-7401"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngji Kim","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-0497-7401","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049607975","display_name":"Sungho Yoon","orcid":"https://orcid.org/0000-0002-5801-9279"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungho Yoon","raw_affiliation_strings":["Robotics Program, KAIST (Korea Advanced Institute of Science and Technology), Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-5801-9279","affiliations":[{"raw_affiliation_string":"Robotics Program, KAIST (Korea Advanced Institute of Science and Technology), Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100774384","display_name":"Su\u2010Jung Kim","orcid":"https://orcid.org/0000-0001-8500-5246"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sujung Kim","raw_affiliation_strings":["Autonomous Driving Group, NAVER LABS, Seongnam, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Autonomous Driving Group, NAVER LABS, Seongnam, South Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740100","display_name":"Ayoung Kim","orcid":"https://orcid.org/0000-0001-9829-2408"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ayoung Kim","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-9829-2408","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.8876,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.97262531,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"6","issue":"2","first_page":"819","last_page":"826"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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":0.9998999834060669,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.7660349607467651},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.6741173267364502},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6595188975334167},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6492077112197876},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.6141072511672974},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.5973285436630249},{"id":"https://openalex.org/keywords/simultaneous-localization-and-mapping","display_name":"Simultaneous localization and mapping","score":0.490998774766922},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.48307421803474426},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4686402976512909},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.45650678873062134},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4344662129878998},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.41700229048728943},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35089606046676636},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.3078920245170593},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.2715039849281311},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2064596712589264},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20595890283584595},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.12677770853042603},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.12512663006782532}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.7660349607467651},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.6741173267364502},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6595188975334167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6492077112197876},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.6141072511672974},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.5973285436630249},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.490998774766922},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.48307421803474426},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4686402976512909},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.45650678873062134},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4344662129878998},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.41700229048728943},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35089606046676636},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.3078920245170593},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.2715039849281311},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2064596712589264},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20595890283584595},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.12677770853042603},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.12512663006782532},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2021.3051571","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2021.3051571","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1479979375","https://openalex.org/W1522301498","https://openalex.org/W1967063885","https://openalex.org/W1992989752","https://openalex.org/W2021930164","https://openalex.org/W2033819227","https://openalex.org/W2042012466","https://openalex.org/W2133352538","https://openalex.org/W2150066425","https://openalex.org/W2182229738","https://openalex.org/W2482726005","https://openalex.org/W2535547924","https://openalex.org/W2582488595","https://openalex.org/W2600383743","https://openalex.org/W2745859992","https://openalex.org/W2765767940","https://openalex.org/W2890955732","https://openalex.org/W2919203733","https://openalex.org/W2938170127","https://openalex.org/W2963423603","https://openalex.org/W2963706662","https://openalex.org/W2963866045","https://openalex.org/W2964121744","https://openalex.org/W2964314455","https://openalex.org/W2969121634","https://openalex.org/W2969202254","https://openalex.org/W2985775862","https://openalex.org/W2991006905","https://openalex.org/W3035056458","https://openalex.org/W3038975720","https://openalex.org/W3090160518","https://openalex.org/W3101037136","https://openalex.org/W3102327032","https://openalex.org/W3125449081","https://openalex.org/W6631190155","https://openalex.org/W6658838613","https://openalex.org/W6685664056","https://openalex.org/W6747834056","https://openalex.org/W6771174174"],"related_works":["https://openalex.org/W2970345194","https://openalex.org/W4386821976","https://openalex.org/W2149015029","https://openalex.org/W4313288997","https://openalex.org/W2807473852","https://openalex.org/W4308497005","https://openalex.org/W48401697","https://openalex.org/W2152464524","https://openalex.org/W2081798451","https://openalex.org/W69681753"],"abstract_inverted_index":{"Incorporating":[0],"multi-sensor,":[1],"in":[2,18,73,140,167],"filter-based":[3],"as":[4,6,98],"well":[5],"graph-based":[7],"simultaneous":[8],"localization":[9],"and":[10,49,55,179],"mapping":[11],"(SLAM),":[12],"relies":[13],"on":[14,41],"the":[15,78,105,129,141,155,160,173],"uncertainties":[16],"involved":[17],"each":[19],"measurement.":[20],"Proper":[21],"covariance":[22,37,48,97,146],"estimation":[23,63],"is":[24],"thus":[25],"critical":[26],"to":[27,52,69,90,154,182],"balance":[28],"confidence":[29],"levels":[30],"among":[31],"sensors.":[32],"Despite":[33],"its":[34],"importance,":[35],"traditional":[36,79],"approximation":[38],"mostly":[39],"relied":[40],"first":[42],"order":[43],"derivative":[44],"or":[45],"fixed":[46],"measurement":[47],"therefore":[50],"tended":[51],"be":[53],"error-prone,":[54],"even":[56],"heuristic.":[57],"Recently,":[58],"deep":[59],"learning":[60,108,147,163],"for":[61,87,148],"uncertainty":[62,88,92,112,119,130,162],"yielded":[64],"meaningful":[65],"performance,":[66],"but":[67],"applied":[68],"a":[70,74,99,109,115,168],"single":[71,110],"sensor":[72,122],"supervised":[75,80],"manner.":[76],"Unlike":[77],"manner,":[81],"we":[82,103,127],"introduce":[83],"an":[84],"unsupervised":[85],"loss":[86],"modeling,":[89],"learn":[91],"without":[93],"needing":[94],"ground":[95],"truth":[96],"label.":[100],"Most":[101],"important,":[102],"overcome":[104],"limitation":[106],"of":[107,117,157],"sensor's":[111],"by":[113],"introducing":[114],"way":[116],"balancing":[118,131],"between":[120,133],"different":[121],"modalities.":[123],"In":[124],"doing":[125],"so,":[126],"alleviate":[128],"issue":[132],"sensors":[134],"that":[135],"has":[136],"often":[137],"been":[138],"encountered":[139],"multi-sensor":[142],"SLAM":[143],"application.":[144],"Targeting":[145],"visual":[149,178],"odometry,":[150],"particularly":[151],"with":[152],"regard":[153],"integration":[156],"inertial":[158,180],"sensors,":[159],"proposed":[161],"method":[164],"was":[165],"validated":[166],"visual-inertial":[169],"odometry":[170],"application":[171],"over":[172],"public":[174],"datasets":[175],"under":[176],"artificial":[177],"degradations":[181],"mimic":[183],"harsh":[184],"environment.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
