{"id":"https://openalex.org/W2798302276","doi":"https://doi.org/10.1109/lra.2018.2869640","title":"VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry","display_name":"VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry","publication_year":2018,"publication_date":"2018-09-13","ids":{"openalex":"https://openalex.org/W2798302276","doi":"https://doi.org/10.1109/lra.2018.2869640","mag":"2798302276"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2018.2869640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2018.2869640","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1804.08366","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Noha Radwan","orcid":"https://orcid.org/0000-0003-1405-845X"},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Noha Radwan","raw_affiliation_strings":["Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Abhinav Valada","orcid":"https://orcid.org/0000-0003-4710-3114"},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Abhinav Valada","raw_affiliation_strings":["Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wolfram Burgard","orcid":"https://orcid.org/0000-0002-5680-6500"},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wolfram Burgard","raw_affiliation_strings":["Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","institution_ids":["https://openalex.org/I161046081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161046081"],"apc_list":null,"apc_paid":null,"fwci":169.8697,"has_fulltext":false,"cited_by_count":200,"citation_normalized_percentile":{"value":0.99967249,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"3","issue":"4","first_page":"4407","last_page":"4414"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.8756999969482422,"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":0.8756999969482422,"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":0.030400000512599945,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.012500000186264515,"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":0.6007999777793884},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5235999822616577},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.49549999833106995},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4867999851703644},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.4153999984264374},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.40639999508857727},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4041999876499176},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.3889000117778778},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3862999975681305},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.3736000061035156}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839000225067139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7659000158309937},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6007999777793884},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5235999822616577},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4867999851703644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4546999931335449},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.4153999984264374},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.3736000061035156},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.3343000113964081},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.28850001096725464},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26330000162124634}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lra.2018.2869640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2018.2869640","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1804.08366","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.08366","pdf_url":"https://arxiv.org/pdf/1804.08366","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":null,"raw_type":"text"},{"id":"pmh:oai:freidok.uni-freiburg.de:270951","is_oa":false,"landing_page_url":"https://freidok.uni-freiburg.de/data/270951","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Robotics and automation letters. - 3, 4 (2018) , 4407-4414, ISSN: 2377-3766","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1804.08366","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.08366","pdf_url":"https://arxiv.org/pdf/1804.08366","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1907502433","https://openalex.org/W1907729166","https://openalex.org/W2592936284","https://openalex.org/W2765793020","https://openalex.org/W6638480814","https://openalex.org/W6640054144","https://openalex.org/W6647685828","https://openalex.org/W6687522706","https://openalex.org/W6695206952","https://openalex.org/W6712678400","https://openalex.org/W6730099391","https://openalex.org/W6733346999","https://openalex.org/W6736755255","https://openalex.org/W6738279954","https://openalex.org/W6741335108","https://openalex.org/W6746605782","https://openalex.org/W6749370968"],"related_works":["https://openalex.org/W3161199934","https://openalex.org/W2980486736","https://openalex.org/W813170165","https://openalex.org/W2126722086","https://openalex.org/W2182126089","https://openalex.org/W1617689420","https://openalex.org/W2168170647","https://openalex.org/W124472766","https://openalex.org/W2156168935","https://openalex.org/W2256733655"],"abstract_inverted_index":{"Semantic":[0],"understanding":[1,34],"and":[2,84,105,129,180,195,215],"localization":[3,136,174],"are":[4],"fundamental":[5],"enablers":[6],"of":[7,32,54,90,92,108],"robot":[8],"autonomy":[9],"that":[10,50,66,149,200],"have":[11],"been":[12,43],"tackled":[13],"as":[14],"disjoint":[15],"problems":[16],"for":[17,86,163,183],"the":[18,47,63,74,87,98,109,112,135,151,160,190,204],"most":[19],"part.":[20],"While":[21],"deep":[22,185],"learning":[23,70,78,164],"has":[24,42],"enabled":[25],"recent":[26],"breakthroughs":[27],"across":[28],"a":[29,68,118,145,170],"wide":[30],"spectrum":[31],"scene":[33],"tasks,":[35],"its":[36],"applicability":[37],"to":[38,46,72,124,130,154],"state":[39],"estimation":[40],"tasks":[41,214],"limited":[44],"due":[45],"direct":[48],"formulation":[49],"renders":[51],"it":[52],"incapable":[53],"encoding":[55],"scene-specific":[56],"constrains.":[57],"In":[58],"this":[59],"letter,":[60],"we":[61,143,168],"propose":[62,117,144],"VLocNet++":[64],"architecture":[65],"employs":[67],"multitask":[69],"approach":[71,202],"exploit":[73],"inter-task":[75],"relationship":[76],"between":[77],"semantics,":[79],"regressing":[80],"6-DoF":[81],"global":[82],"pose":[83,113],"odometry,":[85],"mutual":[88],"benefit":[89],"each":[91],"these":[93],"tasks.":[94],"Our":[95],"network":[96,157],"overcomes":[97],"aforementioned":[99],"limitation":[100],"by":[101],"simultaneously":[102,211],"embedding":[103],"geometric":[104],"semantic":[106,132,178],"knowledge":[107],"world":[110],"into":[111,134],"regression":[114],"network.":[115],"We":[116],"novel":[119],"adaptive":[120],"weighted":[121],"fusion":[122],"layer":[123],"aggregate":[125],"motion-specific":[126],"temporal":[127],"information":[128],"fuse":[131],"features":[133],"stream":[137,162],"based":[138],"on":[139,189],"region":[140],"activations.":[141],"Furthermore,":[142],"self-supervised":[146],"warping":[147],"technique":[148],"uses":[150],"relative":[152],"motion":[153],"warp":[155],"intermediate":[156],"representations":[158],"in":[159,219],"segmentation":[161],"consistent":[165],"semantics.":[166],"Finally,":[167],"introduce":[169],"first-of-a-kind":[171],"urban":[172],"outdoor":[173],"dataset":[175,198],"with":[176],"pixel-level":[177],"labels":[179],"multiple":[181,213],"loops":[182],"training":[184],"networks.":[186],"Extensive":[187],"experiments":[188],"challenging":[191,220],"Microsoft":[192],"7-Scenes":[193],"benchmark":[194],"our":[196,201],"DeepLoc":[197],"demonstrate":[199],"exceeds":[203],"state-of-the-art":[205],"outperforming":[206],"local":[207],"feature-based":[208],"methods":[209],"while":[210],"performing":[212],"exhibiting":[216],"substantial":[217],"robustness":[218],"scenarios.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":34},{"year":2022,"cited_by_count":36},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":34},{"year":2019,"cited_by_count":19}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2018-05-07T00:00:00"}