{"id":"https://openalex.org/W3027789438","doi":"https://doi.org/10.1109/icarsc49921.2020.9096138","title":"Multimodal Deep-Learning for Object Recognition Combining Camera and LIDAR Data","display_name":"Multimodal Deep-Learning for Object Recognition Combining Camera and LIDAR Data","publication_year":2020,"publication_date":"2020-04-01","ids":{"openalex":"https://openalex.org/W3027789438","doi":"https://doi.org/10.1109/icarsc49921.2020.9096138","mag":"3027789438"},"language":"en","primary_location":{"id":"doi:10.1109/icarsc49921.2020.9096138","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarsc49921.2020.9096138","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)","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/A5084838708","display_name":"Gledson Melotti","orcid":"https://orcid.org/0000-0002-8988-0205"},"institutions":[{"id":"https://openalex.org/I4210160371","display_name":"Instituto Federal do Esp\u00edrito Santo","ror":"https://ror.org/05rshs160","country_code":"BR","type":"funder","lineage":["https://openalex.org/I4210160371"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Gledson Melotti","raw_affiliation_strings":["Federal Institute of Espirito Santo, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal Institute of Espirito Santo, Brazil","institution_ids":["https://openalex.org/I4210160371"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039508418","display_name":"Cristiano Premebida","orcid":"https://orcid.org/0000-0002-2168-2077"},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]},{"id":"https://openalex.org/I4210125590","display_name":"Institute for Systems Engineering and Computers","ror":"https://ror.org/033wn8m60","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Cristiano Premebida","raw_affiliation_strings":["Institute of Systems and Robotics, University of Coimbra, Portugal"],"affiliations":[{"raw_affiliation_string":"Institute of Systems and Robotics, University of Coimbra, Portugal","institution_ids":["https://openalex.org/I4210125590","https://openalex.org/I76903346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060785558","display_name":"Nuno Gon\u00e7alves","orcid":"https://orcid.org/0000-0002-1854-049X"},"institutions":[{"id":"https://openalex.org/I4210125590","display_name":"Institute for Systems Engineering and Computers","ror":"https://ror.org/033wn8m60","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590"]},{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Nuno Goncalves","raw_affiliation_strings":["Institute of Systems and Robotics, University of Coimbra, Portugal"],"affiliations":[{"raw_affiliation_string":"Institute of Systems and Robotics, University of Coimbra, Portugal","institution_ids":["https://openalex.org/I4210125590","https://openalex.org/I76903346"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084838708"],"corresponding_institution_ids":["https://openalex.org/I4210160371"],"apc_list":null,"apc_paid":null,"fwci":42.4277,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.99545447,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"177","last_page":"182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994000196456909,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8968740701675415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8083659410476685},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7480053305625916},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7183573246002197},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6648977994918823},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5449210405349731},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.5194510817527771},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5080165863037109},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4662904143333435},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37738874554634094},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2833706736564636},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16730350255966187},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1395288109779358}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8968740701675415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8083659410476685},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7480053305625916},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7183573246002197},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6648977994918823},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5449210405349731},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.5194510817527771},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5080165863037109},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4662904143333435},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37738874554634094},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2833706736564636},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16730350255966187},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1395288109779358}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarsc49921.2020.9096138","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarsc49921.2020.9096138","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W22229905","https://openalex.org/W294926941","https://openalex.org/W1492815834","https://openalex.org/W1642501641","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1990037396","https://openalex.org/W2006488212","https://openalex.org/W2097117768","https://openalex.org/W2115579991","https://openalex.org/W2150066425","https://openalex.org/W2163605009","https://openalex.org/W2164507085","https://openalex.org/W2194775991","https://openalex.org/W2342662179","https://openalex.org/W2416791088","https://openalex.org/W2520194964","https://openalex.org/W2560609797","https://openalex.org/W2605364946","https://openalex.org/W2732026016","https://openalex.org/W2735596698","https://openalex.org/W2752646893","https://openalex.org/W2775018596","https://openalex.org/W2810240468","https://openalex.org/W2897083303","https://openalex.org/W2899960484","https://openalex.org/W2900172222","https://openalex.org/W2905005563","https://openalex.org/W2919115771","https://openalex.org/W2955544543","https://openalex.org/W2962771259","https://openalex.org/W2962818872","https://openalex.org/W2962987395","https://openalex.org/W2963121255","https://openalex.org/W2963432933","https://openalex.org/W2963954891","https://openalex.org/W2972321440","https://openalex.org/W3021868751","https://openalex.org/W3040318838","https://openalex.org/W4300124531","https://openalex.org/W6610624339","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6737309142","https://openalex.org/W6739778489","https://openalex.org/W6763422710","https://openalex.org/W6767375921","https://openalex.org/W6776459209"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W4293094720","https://openalex.org/W2739701376","https://openalex.org/W3176213335","https://openalex.org/W4361296799"],"abstract_inverted_index":{"Object":[0],"detection":[1,28],"and":[2,29,41,59,115,185,204,222,226],"recognition":[3],"is":[4,54,75,106,134,175,198],"a":[5,36,64,85,96,161],"key":[6],"component":[7],"of":[8,63,70,141,144,148,188,216],"autonomous":[9],"robotic":[10,21],"vehicles,":[11],"as":[12,56,108,155,160],"evidenced":[13],"by":[14,19,61,77,90,157],"the":[15,20,68,79,112,117,130,138,142,150,153,158,166,179,182,186,195,202,207],"continuous":[16],"efforts":[17],"made":[18],"community":[22],"on":[23,38,111,116,129,194],"areas":[24],"related":[25],"to":[26,152,164,200,205],"object":[27,47],"sensory":[30],"perception":[31],"systems.":[32],"This":[33],"paper":[34],"presents":[35],"study":[37],"multisensor":[39],"(camera":[40],"LIDAR)":[42],"late":[43,227],"fusion":[44,228],"strategies":[45],"for":[46],"recognition.":[48],"In":[49],"this":[50,145],"work,":[51],"LIDAR":[52,80,223],"data":[53],"processed":[55],"3D":[57,81,131],"points":[58],"also":[60,135],"means":[62],"2D":[65,86,98],"representation":[66],"in":[67,137,214],"form":[69],"depth":[71],"map":[72],"(DM),":[73],"which":[74,94,125,177],"obtained":[76],"projecting":[78],"point":[82,132],"cloud":[83],"into":[84],"image":[87],"plane":[88],"followed":[89],"an":[91],"upsampling":[92],"strategy":[93,174],"generates":[95],"high-resolution":[97],"range":[99],"view.":[100],"A":[101,121,169,190],"CNN":[102],"network":[103],"(Inception":[104],"V3)":[105],"used":[107,199],"classification":[109,128,167,191],"method":[110],"RGB":[113,221],"images,":[114],"DMs":[118],"(LIDAR":[119],"modality).":[120],"3D-network":[122],"(the":[123],"PointNet),":[124],"directly":[126],"performs":[127],"clouds,":[133],"considered":[136],"experiments.":[139],"One":[140],"motivations":[143],"work":[146],"consists":[147],"incorporating":[149],"distance":[151,187],"objects,":[154],"measured":[156],"LIDAR,":[159],"relevant":[162],"cue":[163],"improve":[165],"performance.":[168],"new":[170],"range-based":[171],"average":[172],"weighting":[173],"proposed,":[176],"considers":[178],"relationship":[180],"between":[181],"deep-models'":[183],"performance":[184],"objects.":[189],"dataset,":[192],"based":[193],"KITTI":[196],"database,":[197],"evaluate":[201],"deep-models,":[203],"support":[206],"experimental":[208],"part.":[209],"We":[210],"report":[211],"extensive":[212],"results":[213],"terms":[215],"single":[217],"modality":[218],"i.e.,":[219],"using":[220],"models":[224],"individually,":[225],"multimodality":[229],"approaches.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
