{"id":"https://openalex.org/W4416676827","doi":"https://doi.org/10.1109/sbr/wre66973.2025.11249574","title":"Semantic Segmentation Network for Warehouse Indoor Environments from 3D LiDAR Point Clouds","display_name":"Semantic Segmentation Network for Warehouse Indoor Environments from 3D LiDAR Point Clouds","publication_year":2025,"publication_date":"2025-10-13","ids":{"openalex":"https://openalex.org/W4416676827","doi":"https://doi.org/10.1109/sbr/wre66973.2025.11249574"},"language":null,"primary_location":{"id":"doi:10.1109/sbr/wre66973.2025.11249574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sbr/wre66973.2025.11249574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Brazilian Symposium on Robotics (SBR) and 2025 Workshop on Robotics in Education (WRE)","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":null,"display_name":"Gabriel do Pinho Martins","orcid":"https://orcid.org/0009-0005-9324-4438"},"institutions":[{"id":"https://openalex.org/I126460647","display_name":"Universidade Federal do Rio Grande","ror":"https://ror.org/05hpfkn88","country_code":"BR","type":"education","lineage":["https://openalex.org/I126460647"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gabriel do Pinho Martins","raw_affiliation_strings":["Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil"],"raw_orcid":"https://orcid.org/0009-0005-9324-4438","affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil","institution_ids":["https://openalex.org/I126460647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020192402","display_name":"Kristofer S. Kappel","orcid":"https://orcid.org/0000-0002-9124-8540"},"institutions":[{"id":"https://openalex.org/I126460647","display_name":"Universidade Federal do Rio Grande","ror":"https://ror.org/05hpfkn88","country_code":"BR","type":"education","lineage":["https://openalex.org/I126460647"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Kristofer Stift Kappel","raw_affiliation_strings":["Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil"],"raw_orcid":"https://orcid.org/0000-0002-9124-8540","affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil","institution_ids":["https://openalex.org/I126460647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000693027","display_name":"Vinicius Leite de Garcia","orcid":"https://orcid.org/0009-0001-2087-2055"},"institutions":[{"id":"https://openalex.org/I126460647","display_name":"Universidade Federal do Rio Grande","ror":"https://ror.org/05hpfkn88","country_code":"BR","type":"education","lineage":["https://openalex.org/I126460647"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vinicius Leite de Garcia","raw_affiliation_strings":["Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil"],"raw_orcid":"https://orcid.org/0009-0001-2087-2055","affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil","institution_ids":["https://openalex.org/I126460647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004181756","display_name":"Felipe G. Oliveira","orcid":"https://orcid.org/0000-0002-5435-0933"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Felipe Gomes de Oliveira","raw_affiliation_strings":["Instituto de Ciências Exatas e Tecnologia, Universidade Federal do Amazonas,Itacoatiara,Brazil"],"raw_orcid":"https://orcid.org/0000-0002-5435-0933","affiliations":[{"raw_affiliation_string":"Instituto de Ciências Exatas e Tecnologia, Universidade Federal do Amazonas,Itacoatiara,Brazil","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071907325","display_name":"Nelson Duarte","orcid":"https://orcid.org/0000-0002-6351-6843"},"institutions":[{"id":"https://openalex.org/I126460647","display_name":"Universidade Federal do Rio Grande","ror":"https://ror.org/05hpfkn88","country_code":"BR","type":"education","lineage":["https://openalex.org/I126460647"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Nelson Duarte Filho","raw_affiliation_strings":["Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil"],"raw_orcid":"https://orcid.org/0000-0002-6351-6843","affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil","institution_ids":["https://openalex.org/I126460647"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037041582","display_name":"Paulo Drews","orcid":"https://orcid.org/0000-0002-7519-0502"},"institutions":[{"id":"https://openalex.org/I126460647","display_name":"Universidade Federal do Rio Grande","ror":"https://ror.org/05hpfkn88","country_code":"BR","type":"education","lineage":["https://openalex.org/I126460647"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Paulo Lilles Jorge Drews","raw_affiliation_strings":["Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil"],"raw_orcid":"https://orcid.org/0000-0002-7519-0502","affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio Grande,Centro de Ciências Computacionais,Rio Grande,Brazil","institution_ids":["https://openalex.org/I126460647"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42027662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"225","last_page":"230"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.5329999923706055,"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":0.5329999923706055,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.20589999854564667,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.11349999904632568,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7257999777793884},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7184000015258789},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6873000264167786},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6646000146865845},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.621999979019165},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.45089998841285706},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.44269999861717224},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4381999969482422},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.43309998512268066}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7257999777793884},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7184000015258789},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7027000188827515},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6873000264167786},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6646000146865845},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5809999704360962},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4381999969482422},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.43309998512268066},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3601999878883362},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3553999960422516},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2870999872684479},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sbr/wre66973.2025.11249574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sbr/wre66973.2025.11249574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Brazilian Symposium on Robotics (SBR) and 2025 Workshop on Robotics in Education (WRE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2888375042","https://openalex.org/W2968557240","https://openalex.org/W3003437478","https://openalex.org/W3186678659","https://openalex.org/W4210986463","https://openalex.org/W4312419284","https://openalex.org/W4315778466","https://openalex.org/W4400911769","https://openalex.org/W4402753960"],"related_works":[],"abstract_inverted_index":{"Robust":[0],"perception":[1],"is":[2,201],"paramount":[3],"for":[4,17,23,47,74,96,129,165,171],"autonomous":[5,24],"systems":[6],"that":[7],"operate":[8],"in":[9,31,139,173],"real":[10],"public":[11],"environments.":[12,85],"LiDAR":[13,41,79],"sensors":[14],"are":[15,45],"known":[16],"their":[18],"robustness,":[19],"making":[20],"them":[21],"ideal":[22],"robots,":[25],"which":[26],"enable":[27],"obstacle":[28],"detection,":[29],"reliability":[30],"adverse":[32],"lighting":[33],"conditions,":[34],"and":[35,49,106,123,132,150,163,167,169,175,187],"overall":[36],"safety":[37],"assurance.":[38],"Most":[39],"3D":[40,78,97],"semantic":[42,75,98],"segmentation":[43,76,99],"networks":[44],"fine-tuned":[46],"outdoor":[48],"urban":[50],"environments,":[51],"overlooking":[52],"other":[53],"equally":[54],"important":[55],"scenes,":[56],"such":[57],"as":[58],"industrial":[59,93],"scenarios.":[60],"In":[61],"this":[62],"work,":[63],"we":[64,87],"contribute":[65],"to":[66],"an":[67,140],"open-source":[68],"Convolutional":[69],"Neural":[70],"Network":[71],"(CNN)":[72],"pipeline":[73],"of":[77,82,91,108,161],"point":[80,94],"clouds":[81,95],"indoor":[83],"warehouse":[84,142],"Additionally,":[86,182],"introduce":[88],"a":[89],"dataset":[90,149,200],"simulated":[92],"called":[100],"WareSet.":[101],"We":[102],"evaluate":[103],"the":[104,147,151,191],"quality":[105,113,153],"viability":[107],"our":[109],"network":[110],"using":[111],"well-defined":[112],"metrics,":[114],"including":[115],"accuracy,":[116,166],"Intersection":[117,125],"over":[118,126],"Union":[119,127],"(IoU)":[120],"per":[121],"class,":[122],"mean":[124],"(mIoU),":[128],"both":[130],"training":[131,174],"validation":[133],"sets.":[134],"The":[135,155,198],"experiments":[136],"were":[137],"conducted":[138],"unfamiliar":[141],"environment,":[143],"taking":[144],"into":[145],"account":[146],"proposed":[148,156,199],"specified":[152],"metrics.":[154],"approach":[157],"yields":[158],"satisfactory":[159],"results":[160],"98.49%":[162],"90.59%":[164],"90.64%":[168],"49.49%":[170],"mIoU":[172,189],"validation,":[176],"respectively,":[177],"with":[178],"good":[179],"model":[180],"convergence.":[181],"it":[183],"achieves":[184],"89.7%":[185],"accuracy":[186],"50.4%":[188],"on":[190],"unknown":[192],"warehouse,":[193],"emphasizing":[194],"its":[195],"generalization":[196],"capability.":[197],"open-sourced":[202],"at":[203],"https://github.com/DeskFanzin/WareSet.":[204]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-25T00:00:00"}