{"id":"https://openalex.org/W2890955732","doi":"https://doi.org/10.1109/icra.2018.8461047","title":"Deep Inference for Covariance Estimation: Learning Gaussian Noise Models for State Estimation","display_name":"Deep Inference for Covariance Estimation: Learning Gaussian Noise Models for State Estimation","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2890955732","doi":"https://doi.org/10.1109/icra.2018.8461047","mag":"2890955732"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2018.8461047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8461047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/1721.1/123673","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091371230","display_name":"Katherine Liu","orcid":"https://orcid.org/0000-0002-9616-9403"},"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":true,"raw_author_name":"Katherine Liu","raw_affiliation_strings":["Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063242159","display_name":"Kyel Ok","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":"Kyel Ok","raw_affiliation_strings":["Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021455082","display_name":"William Vega-Brown","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":"William Vega-Brown","raw_affiliation_strings":["Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004980809","display_name":"Nicholas Roy","orcid":"https://orcid.org/0000-0002-4277-2928"},"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":"Nicholas Roy","raw_affiliation_strings":["Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5091371230"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":4.7328,"has_fulltext":false,"cited_by_count":57,"citation_normalized_percentile":{"value":0.95892125,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1436","last_page":"1443"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9958999752998352,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9832000136375427,"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/covariance","display_name":"Covariance","score":0.7857202291488647},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.7032493948936462},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6932604312896729},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.5764686465263367},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5502670407295227},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.5232469439506531},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.48351719975471497},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48146477341651917},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4719345271587372},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.45583534240722656},{"id":"https://openalex.org/keywords/mat\u00e9rn-covariance-function","display_name":"Mat\u00e9rn covariance function","score":0.4360645115375519},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.4276028275489807},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.42220449447631836},{"id":"https://openalex.org/keywords/rational-quadratic-covariance-function","display_name":"Rational quadratic covariance function","score":0.41977596282958984},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39206698536872864},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.3030378818511963},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1949516236782074},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1470995545387268}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.7857202291488647},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.7032493948936462},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6932604312896729},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.5764686465263367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5502670407295227},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.5232469439506531},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.48351719975471497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48146477341651917},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4719345271587372},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.45583534240722656},{"id":"https://openalex.org/C118006245","wikidata":"https://www.wikidata.org/wiki/Q6792079","display_name":"Mat\u00e9rn covariance function","level":5,"score":0.4360645115375519},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.4276028275489807},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.42220449447631836},{"id":"https://openalex.org/C148893098","wikidata":"https://www.wikidata.org/wiki/Q7295778","display_name":"Rational quadratic covariance function","level":5,"score":0.41977596282958984},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39206698536872864},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.3030378818511963},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1949516236782074},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1470995545387268},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2018.8461047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8461047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:dspace.mit.edu:1721.1/123673","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/123673","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":"Katherine Liu","raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/123673","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/123673","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":"Katherine Liu","raw_type":"http://purl.org/eprint/type/ConferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W612478963","https://openalex.org/W1479979375","https://openalex.org/W1686810756","https://openalex.org/W2007692097","https://openalex.org/W2021851106","https://openalex.org/W2021930164","https://openalex.org/W2024228246","https://openalex.org/W2064169939","https://openalex.org/W2074232022","https://openalex.org/W2101033242","https://openalex.org/W2120480077","https://openalex.org/W2133352538","https://openalex.org/W2147552081","https://openalex.org/W2156064084","https://openalex.org/W2169901455","https://openalex.org/W2212012417","https://openalex.org/W2255696880","https://openalex.org/W2419396125","https://openalex.org/W2563015642","https://openalex.org/W2600383743","https://openalex.org/W2736160108","https://openalex.org/W2765767940","https://openalex.org/W3104991028","https://openalex.org/W6618872416","https://openalex.org/W6628836592","https://openalex.org/W6655830098","https://openalex.org/W6656063324","https://openalex.org/W6666603138","https://openalex.org/W6678242812","https://openalex.org/W6684846091","https://openalex.org/W6688229647","https://openalex.org/W6717267025","https://openalex.org/W6730974727","https://openalex.org/W6735443497","https://openalex.org/W6741297073","https://openalex.org/W6745329321","https://openalex.org/W6785802668"],"related_works":["https://openalex.org/W2022823194","https://openalex.org/W2126916073","https://openalex.org/W3211883524","https://openalex.org/W2109377650","https://openalex.org/W1987404909","https://openalex.org/W4386993326","https://openalex.org/W1627656821","https://openalex.org/W3025277714","https://openalex.org/W2050026105","https://openalex.org/W1467295286"],"abstract_inverted_index":{"We":[0,78],"present":[1],"a":[2,14,62,71,94],"novel":[3],"method":[4],"of":[5,16,34,46,70,83,96,107],"measurement":[6,11,18,73,86,89,98],"covariance":[7,69,127],"estimation":[8],"that":[9,37,80],"models":[10],"uncertainty":[12],"as":[13],"function":[15],"the":[17,35,40,43,47,68,97,104,108,111],"itself.":[19],"Existing":[20],"work":[21],"in":[22],"predictive":[23],"sensor":[24,72,76,85],"modeling":[25],"outperforms":[26],"conventional":[27],"fixed":[28],"models,":[29],"but":[30],"requires":[31],"domain":[32],"knowledge":[33],"sensors":[36],"heavily":[38],"influences":[39],"accuracy":[41],"and":[42,87,116,123,132],"computational":[44],"cost":[45],"models.":[48],"In":[49],"this":[50],"work,":[51],"we":[52,91,124],"introduce":[53],"Deep":[54],"Inference":[55],"for":[56,113],"Covariance":[57],"Estimation":[58],"(DICE),":[59],"which":[60],"utilizes":[61],"deep":[63],"neural":[64],"network":[65],"to":[66],"predict":[67],"from":[74],"raw":[75,84],"data.":[77,134],"show":[79],"given":[81],"pairs":[82],"ground-truth":[88],"error,":[90],"can":[92],"learn":[93],"representation":[95],"model":[99],"via":[100],"supervised":[101],"regression":[102],"on":[103,129],"prediction":[105,128],"performance":[106],"model,":[109],"eliminating":[110],"need":[112],"hand-coded":[114],"features":[115],"parametric":[117],"forms.":[118],"Our":[119],"approach":[120],"is":[121],"sensor-agnostic,":[122],"demonstrate":[125],"improved":[126],"both":[130],"simulated":[131],"real":[133]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":4}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}