{"id":"https://openalex.org/W4416249729","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227558","title":"Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration","display_name":"Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249729","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227558"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5101603187","display_name":"Hui Gao","orcid":"https://orcid.org/0000-0002-9557-7739"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haojia Gao","raw_affiliation_strings":["Beijing University of Technology,Fan Gongxiu Honors College,Beijing,China,100124"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Fan Gongxiu Honors College,Beijing,China,100124","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072165710","display_name":"Haohua Que","orcid":"https://orcid.org/0009-0000-4169-7316"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haohua Que","raw_affiliation_strings":["Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064293007","display_name":"Kunrong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kunrong Li","raw_affiliation_strings":["Beijing University of Technology,Department of College of Computer Science,Beijing,China,100124"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Department of College of Computer Science,Beijing,China,100124","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107836386","display_name":"Weijun Shan","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihao Shan","raw_affiliation_strings":["Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101710160","display_name":"Mingkai Liu","orcid":"https://orcid.org/0000-0001-9444-8794"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingkai Liu","raw_affiliation_strings":["Peking University,School of Software & Microelectronics,Beijing,China,102600"],"affiliations":[{"raw_affiliation_string":"Peking University,School of Software & Microelectronics,Beijing,China,102600","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113354560","display_name":"Rong Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Zhao","raw_affiliation_strings":["Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088938998","display_name":"Lei Mu","orcid":"https://orcid.org/0000-0001-8141-2135"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Mu","raw_affiliation_strings":["Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041303247","display_name":"Xinghua Yang","orcid":"https://orcid.org/0000-0002-4354-1742"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghua Yang","raw_affiliation_strings":["Beijing University of Technology,Fan Gongxiu Honors College,Beijing,China,100124"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Fan Gongxiu Honors College,Beijing,China,100124","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075311656","display_name":"Qi Wei","orcid":"https://orcid.org/0000-0003-3189-7562"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Wei","raw_affiliation_strings":["Tsinghua University,Department of Precision Instrument,Beijing,China,100084"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Precision Instrument,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063901096","display_name":"Fei Qiao","orcid":"https://orcid.org/0000-0002-5054-9590"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Qiao","raw_affiliation_strings":["Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,SenseLab,Department of Electronic Engineering,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5101603187"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46817514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.946399986743927,"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":0.946399986743927,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.007600000128149986,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.006800000090152025,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6740999817848206},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5019000172615051},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38339999318122864},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.37540000677108765},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.314300000667572}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712000012397766},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6740999817848206},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6330999732017517},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5019000172615051},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.37540000677108765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3626999855041504},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3521000146865845},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.314300000667572},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31290000677108765},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":38,"referenced_works":["https://openalex.org/W12154863","https://openalex.org/W1901129140","https://openalex.org/W1966213627","https://openalex.org/W2006003168","https://openalex.org/W2076518796","https://openalex.org/W2089437550","https://openalex.org/W2111481694","https://openalex.org/W2117539524","https://openalex.org/W2133665775","https://openalex.org/W2142924305","https://openalex.org/W2149746310","https://openalex.org/W2156830107","https://openalex.org/W2183341477","https://openalex.org/W2950689937","https://openalex.org/W2962785568","https://openalex.org/W2967333821","https://openalex.org/W2967969632","https://openalex.org/W2968968404","https://openalex.org/W3023175715","https://openalex.org/W3130548322","https://openalex.org/W3196603038","https://openalex.org/W3199003182","https://openalex.org/W3200379731","https://openalex.org/W3201409833","https://openalex.org/W3207246097","https://openalex.org/W3217495337","https://openalex.org/W4212875960","https://openalex.org/W4247999965","https://openalex.org/W4285102272","https://openalex.org/W4317242653","https://openalex.org/W4376464574","https://openalex.org/W4388947967","https://openalex.org/W4402028077","https://openalex.org/W4403398416","https://openalex.org/W4403977964","https://openalex.org/W4405137273","https://openalex.org/W4405270712","https://openalex.org/W4413925576"],"related_works":[],"abstract_inverted_index":{"Autonomous":[0],"exploration":[1,25,68,159,192],"in":[2,9,41,63,147,193],"unknown":[3],"environments":[4],"is":[5],"a":[6,54,99,169,176,185],"critical":[7],"challenge":[8],"robotics,":[10],"particularly":[11],"for":[12,89,116],"applications":[13],"such":[14,27],"as":[15,28],"indoor":[16,42,117],"navigation,":[17],"search":[18],"and":[19,21,78,105,111,136,174],"rescue,":[20],"service":[22],"robotics.":[23],"Traditional":[24],"strategies,":[26],"frontier-based":[29,154],"methods,":[30],"often":[31],"struggle":[32],"to":[33,81,152,165],"efficiently":[34],"utilize":[35],"prior":[36],"knowledge":[37],"of":[38,72,113,129,134,142,179],"structural":[39],"regularities":[40],"spaces.":[43],"To":[44],"address":[45],"this":[46],"limitation,":[47],"we":[48,96],"propose":[49],"Mapping":[50],"at":[51],"First":[52],"Sense,":[53],"lightweight":[55],"neural":[56,114],"network-based":[57],"approach":[58],"that":[59,122],"predicts":[60],"unobserved":[61],"areas":[62],"local":[64],"maps,":[65],"thereby":[66],"enhancing":[67],"efficiency.":[69],"The":[70,181],"core":[71],"our":[73,156],"method,":[74],"SenseMapNet,":[75],"integrates":[76],"convolutional":[77],"transformer-based":[79],"architectures":[80],"infer":[82],"occluded":[83],"regions":[84],"while":[85,167],"maintaining":[86,168],"computational":[87],"efficiency":[88],"real-time":[90],"deployment":[91],"on":[92],"resource-constrained":[93],"robots.":[94],"Additionally,":[95],"introduce":[97],"SenseMapDataset,":[98],"curated":[100],"dataset":[101],"constructed":[102],"from":[103],"KTH":[104],"HouseExpo":[106],"environments,":[107],"which":[108],"facilitates":[109],"training":[110],"evaluation":[112],"models":[115],"exploration.":[118],"Experimental":[119],"results":[120],"demonstrate":[121],"SenseMapNet":[123],"achieves":[124],"an":[125,137],"SSIM":[126],"(structural":[127],"similarity)":[128],"0.78,":[130],"LPIPS":[131],"(perceptual":[132],"quality)":[133],"0.68,":[135],"FID":[138],"(feature":[139],"distribution":[140],"alignment)":[141],"239.79,":[143],"outperforming":[144],"conventional":[145],"methods":[146],"map":[148],"reconstruction":[149,177],"quality.":[150],"Compared":[151],"traditional":[153],"exploration,":[155],"method":[157,183],"reduces":[158],"time":[160],"by":[161],"46.5%":[162],"(from":[163],"2335.56s":[164],"1248.68s)":[166],"high":[170],"coverage":[171],"rate":[172],"(88%)":[173],"achieving":[175],"accuracy":[178],"88%.":[180],"proposed":[182],"represents":[184],"promising":[186],"step":[187],"toward":[188],"efficient,":[189],"learning-driven":[190],"robotic":[191],"structured":[194],"environments.":[195]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}