{"id":"https://openalex.org/W4401415123","doi":"https://doi.org/10.1109/icra57147.2024.10610724","title":"UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments","display_name":"UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments","publication_year":2024,"publication_date":"2024-05-13","ids":{"openalex":"https://openalex.org/W4401415123","doi":"https://doi.org/10.1109/icra57147.2024.10610724"},"language":"en","primary_location":{"id":"doi:10.1109/icra57147.2024.10610724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10610724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-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/A5026102921","display_name":"Samuel Triest","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Samuel Triest","raw_affiliation_strings":["Carnegie Mellon University,Robotics Institute,Pittsburgh,PA,USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University,Robotics Institute,Pittsburgh,PA,USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101809215","display_name":"David D. Fan","orcid":"https://orcid.org/0000-0002-8261-1045"},"institutions":[{"id":"https://openalex.org/I4210105175","display_name":"Mission Heritage Medical Group","ror":"https://ror.org/01jm17996","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210105175"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David D. Fan","raw_affiliation_strings":["Field AI,Mission Viejo,CA,USA"],"affiliations":[{"raw_affiliation_string":"Field AI,Mission Viejo,CA,USA","institution_ids":["https://openalex.org/I4210105175"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032584934","display_name":"Sebastian Scherer","orcid":"https://orcid.org/0000-0002-8373-4688"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sebastian Scherer","raw_affiliation_strings":["Carnegie Mellon University,Robotics Institute,Pittsburgh,PA,USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University,Robotics Institute,Pittsburgh,PA,USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111915088","display_name":"Ali-Akbar Agha-Mohammadi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210105175","display_name":"Mission Heritage Medical Group","ror":"https://ror.org/01jm17996","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210105175"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali-Akbar Agha-Mohammadi","raw_affiliation_strings":["Field AI,Mission Viejo,CA,USA"],"affiliations":[{"raw_affiliation_string":"Field AI,Mission Viejo,CA,USA","institution_ids":["https://openalex.org/I4210105175"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026102921"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":3.0245,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.90772714,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"12627","last_page":"12634"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9941999912261963,"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":0.9941999912261963,"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/T11106","display_name":"Data Management and Algorithms","score":0.9868999719619751,"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"}},{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9793999791145325,"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/computer-science","display_name":"Computer science","score":0.7768357992172241},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.6131309270858765},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.5019524097442627},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.4907113313674927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48502153158187866},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4255698025226593},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07610100507736206}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7768357992172241},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.6131309270858765},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.5019524097442627},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4907113313674927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48502153158187866},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4255698025226593},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07610100507736206},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra57147.2024.10610724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra57147.2024.10610724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1974868116","https://openalex.org/W1984645478","https://openalex.org/W2012875423","https://openalex.org/W2031456685","https://openalex.org/W2082252833","https://openalex.org/W2165736859","https://openalex.org/W2166870098","https://openalex.org/W2288298578","https://openalex.org/W2436494909","https://openalex.org/W2471969791","https://openalex.org/W2788200022","https://openalex.org/W2809054577","https://openalex.org/W2889840352","https://openalex.org/W2901522995","https://openalex.org/W2953127211","https://openalex.org/W2963459246","https://openalex.org/W2964339842","https://openalex.org/W2968296999","https://openalex.org/W3004647014","https://openalex.org/W3095530566","https://openalex.org/W3129559111","https://openalex.org/W3131341185","https://openalex.org/W3132270109","https://openalex.org/W3174083630","https://openalex.org/W3174748154","https://openalex.org/W3183566983","https://openalex.org/W3212567339","https://openalex.org/W4206010013","https://openalex.org/W4236965008","https://openalex.org/W4285102272","https://openalex.org/W4287111299","https://openalex.org/W4287262265","https://openalex.org/W4312749288","https://openalex.org/W4312959444","https://openalex.org/W4383109373","https://openalex.org/W4383171997","https://openalex.org/W4385416313","https://openalex.org/W4385430650","https://openalex.org/W4391794847","https://openalex.org/W6696545063","https://openalex.org/W6758015486","https://openalex.org/W6763422710","https://openalex.org/W6792068349","https://openalex.org/W6798225807"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Traversability":[0],"estimation":[1,20],"in":[2,10,22],"rugged,":[3],"unstructured":[4],"environments":[5,57],"remains":[6],"a":[7,44],"challenging":[8],"problem":[9],"field":[11],"robotics.":[12],"Often,":[13],"the":[14,26,87],"need":[15],"for":[16],"precise,":[17],"accurate":[18],"traversability":[19,103,119],"is":[21,96,99],"direct":[23],"opposition":[24],"to":[25,47,65,101,115,142],"limited":[27],"sensing":[28],"and":[29,91,98,121,137,144],"compute":[30],"capability":[31],"present":[32,43],"on":[33,148],"affordable,":[34],"small-scale":[35],"mobile":[36],"robots.":[37],"To":[38],"address":[39],"this":[40],"issue,":[41],"we":[42,109],"novel":[45],"method":[46,132],"learn":[48],"[u]ncertainty-aware":[49],"[n]avigation":[50],"features":[51,69,79],"from":[52,72,81],"high-fidelity":[53,68],"scans":[54],"of":[55,89],"[real]-world":[56],"(UNRealNet).":[58],"This":[59],"network":[60],"can":[61,110],"be":[62],"deployed":[63],"on-robot":[64],"predict":[66],"these":[67],"using":[70],"input":[71],"lower-quality":[73],"sensors.":[74],"UNRealNet":[75],"predicts":[76],"dense,":[77],"metric-space":[78],"directly":[80],"single-frame":[82],"lidar":[83],"scans,":[84],"thus":[85],"reducing":[86],"effects":[88],"occlusion":[90],"odometry":[92],"error.":[93],"Our":[94],"approach":[95],"label-free,":[97],"able":[100],"produce":[102,117],"estimates":[104],"that":[105,130],"are":[106],"robot-agnostic.":[107],"Additionally,":[108],"leverage":[111],"UNRealNet\u2019s":[112],"predictive":[113],"uncertainty":[114],"both":[116],"risk-aware":[118],"estimates,":[120],"refine":[122],"our":[123,131],"feature":[124],"predictions":[125],"over":[126],"time.":[127],"We":[128],"find":[129],"outperforms":[133],"traditional":[134],"local":[135],"mapping":[136],"inpainting":[138],"baselines":[139],"by":[140],"up":[141],"40%,":[143],"demonstrate":[145],"its":[146],"efficacy":[147],"multiple":[149],"legged":[150],"platforms.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}
