{"id":"https://openalex.org/W3143648836","doi":"https://doi.org/10.1109/icra48506.2021.9560957","title":"Out-of-Distribution Robustness with Deep Recursive Filters","display_name":"Out-of-Distribution Robustness with Deep Recursive Filters","publication_year":2021,"publication_date":"2021-05-30","ids":{"openalex":"https://openalex.org/W3143648836","doi":"https://doi.org/10.1109/icra48506.2021.9560957","mag":"3143648836"},"language":"en","primary_location":{"id":"doi:10.1109/icra48506.2021.9560957","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9560957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2104.02799","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044895481","display_name":"Kapil D. Katyal","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]},{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kapil D. Katyal","raw_affiliation_strings":["Dept. of Comp. Sci., Johns Hopkins University, Baltimore, MD, USA","Johns Hopkins University Applied Physics Lab, Laurel, MD, USA","APPLIED PHYSICS LABORATORY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Comp. Sci., Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Johns Hopkins University Applied Physics Lab, Laurel, MD, USA","institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]},{"raw_affiliation_string":"APPLIED PHYSICS LABORATORY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109618222","display_name":"I-Jeng Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]},{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"I-Jeng Wang","raw_affiliation_strings":["Johns Hopkins University Applied Physics Lab, Laurel, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University Applied Physics Lab, Laurel, MD, USA","institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041383246","display_name":"Gregory D. Hager","orcid":"https://orcid.org/0000-0002-6662-9763"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory D. Hager","raw_affiliation_strings":["Dept. of Comp. Sci., Johns Hopkins University, Baltimore, MD, USA","Johns Hopkins University,Dept. of Comp. Sci.,Baltimore,MD,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Comp. Sci., Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]},{"raw_affiliation_string":"Johns Hopkins University,Dept. of Comp. Sci.,Baltimore,MD,USA","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"14134","last_page":"14140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9988999962806702,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/robustness","display_name":"Robustness (evolution)","score":0.8377100825309753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7289983630180359},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5564330220222473},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.5104607343673706},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5082778334617615},{"id":"https://openalex.org/keywords/estimation-of-distribution-algorithm","display_name":"Estimation of distribution algorithm","score":0.47109681367874146},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.442010760307312},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.42924290895462036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36922359466552734}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8377100825309753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7289983630180359},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5564330220222473},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.5104607343673706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5082778334617615},{"id":"https://openalex.org/C162500139","wikidata":"https://www.wikidata.org/wiki/Q2835887","display_name":"Estimation of distribution algorithm","level":2,"score":0.47109681367874146},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.442010760307312},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.42924290895462036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36922359466552734},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icra48506.2021.9560957","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9560957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2104.02799","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.02799","pdf_url":"https://arxiv.org/pdf/2104.02799","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3143648836","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2104.02799","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.2104.02799","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2104.02799","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2104.02799","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.02799","pdf_url":"https://arxiv.org/pdf/2104.02799","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6299999952316284,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3143648836.pdf","grobid_xml":"https://content.openalex.org/works/W3143648836.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W612478963","https://openalex.org/W1560021816","https://openalex.org/W1567512734","https://openalex.org/W1612997784","https://openalex.org/W1980501707","https://openalex.org/W2047448918","https://openalex.org/W2067796266","https://openalex.org/W2176035349","https://openalex.org/W2336416123","https://openalex.org/W2600383743","https://openalex.org/W2788433952","https://openalex.org/W2805116782","https://openalex.org/W2810087315","https://openalex.org/W2912752824","https://openalex.org/W2925148167","https://openalex.org/W2962820842","https://openalex.org/W2962995943","https://openalex.org/W2963166838","https://openalex.org/W2963371290","https://openalex.org/W2963714390","https://openalex.org/W2964059111","https://openalex.org/W2964121744","https://openalex.org/W2968358769","https://openalex.org/W2970859221","https://openalex.org/W2970971581","https://openalex.org/W2971926293","https://openalex.org/W3093010610","https://openalex.org/W3103648783","https://openalex.org/W6617145748","https://openalex.org/W6618872416","https://openalex.org/W6631190155","https://openalex.org/W6685152286","https://openalex.org/W6711952718","https://openalex.org/W6712730493","https://openalex.org/W6735443497","https://openalex.org/W6744063608","https://openalex.org/W6748876104","https://openalex.org/W6753004425","https://openalex.org/W6758371532","https://openalex.org/W6760782946","https://openalex.org/W6763087592","https://openalex.org/W6766978945","https://openalex.org/W6766999186"],"related_works":["https://openalex.org/W3206865772","https://openalex.org/W3104672407","https://openalex.org/W2563954902","https://openalex.org/W3049165116","https://openalex.org/W2908876396","https://openalex.org/W3195487889","https://openalex.org/W3149365522","https://openalex.org/W2963360571","https://openalex.org/W3120421313","https://openalex.org/W2886887402","https://openalex.org/W3200439229","https://openalex.org/W3199064820","https://openalex.org/W2971143061","https://openalex.org/W2765751457","https://openalex.org/W3129243175","https://openalex.org/W3033475332","https://openalex.org/W3133486243","https://openalex.org/W2903995207","https://openalex.org/W3032913293","https://openalex.org/W3141383601"],"abstract_inverted_index":{"Accurate":[0],"state":[1,26,43,48,108,127],"and":[2,10,27,47,95,111,128],"uncertainty":[3,28,77,129],"estimation":[4,29,44,49,78,109,130],"is":[5,33],"imperative":[6],"for":[7,30],"mobile":[8],"robots":[9],"self":[11],"driving":[12],"vehicles":[13],"to":[14,34,53,76,92,132],"achieve":[15],"safe":[16],"navigation":[17,32],"in":[18,80,139],"pedestrian":[19,114],"rich":[20],"environments.":[21],"A":[22],"critical":[23],"component":[24],"of":[25,42,69,98],"robot":[31],"perform":[35],"robustly":[36],"under":[37],"out-of-distribution":[38,93],"noise.":[39],"Traditional":[40],"methods":[41],"decouple":[45],"perception":[46],"making":[50],"it":[51],"difficult":[52],"operate":[54],"on":[55,86,101],"noisy,":[56],"high":[57],"dimensional":[58],"data.":[59],"Here,":[60],"we":[61],"describe":[62],"an":[63],"approach":[64,100,125],"that":[65,88,123],"combines":[66],"the":[67,117],"expressiveness":[68],"deep":[70],"neural":[71],"networks":[72],"with":[73],"principled":[74],"approaches":[75],"found":[79],"recursive":[81],"filters.":[82],"We":[83,121],"particularly":[84],"focus":[85],"techniques":[87],"provide":[89],"better":[90],"robustness":[91],"noise":[94],"demonstrate":[96],"applicability":[97],"our":[99,124],"two":[102],"scenarios:":[103],"a":[104],"simple":[105],"noisy":[106],"pendulum":[107],"problem":[110],"real":[112],"world":[113],"localization":[115],"using":[116],"nuScenes":[118],"dataset":[119],"[1].":[120],"show":[122],"improves":[126],"compared":[131],"baselines":[133],"while":[134],"achieving":[135],"approximately":[136],"3\u00d7":[137],"improvement":[138],"computational":[140],"efficiency.":[141]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}