{"id":"https://openalex.org/W3162795872","doi":"https://doi.org/10.1109/icpr48806.2021.9412043","title":"Deep Next-Best-View Planner for Cross-Season Visual Route Classification","display_name":"Deep Next-Best-View Planner for Cross-Season Visual Route Classification","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3162795872","doi":"https://doi.org/10.1109/icpr48806.2021.9412043","mag":"3162795872"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5030017155","display_name":"Kanya Kurauchi","orcid":null},"institutions":[{"id":"https://openalex.org/I111966504","display_name":"University of Fukui","ror":"https://ror.org/00msqp585","country_code":"JP","type":"education","lineage":["https://openalex.org/I111966504"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kurauchi Kanya","raw_affiliation_strings":["Robotics Course, School of Engineering, University of Fukui, Fukui, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Course, School of Engineering, University of Fukui, Fukui, Japan","institution_ids":["https://openalex.org/I111966504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007423029","display_name":"Tanaka Kanji","orcid":"https://orcid.org/0000-0002-1143-5478"},"institutions":[{"id":"https://openalex.org/I111966504","display_name":"University of Fukui","ror":"https://ror.org/00msqp585","country_code":"JP","type":"education","lineage":["https://openalex.org/I111966504"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tanaka Kanji","raw_affiliation_strings":["Robotics Course, School of Engineering, University of Fukui, Fukui, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Course, School of Engineering, University of Fukui, Fukui, Japan","institution_ids":["https://openalex.org/I111966504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111966504"],"apc_list":null,"apc_paid":null,"fwci":3.329,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.91483421,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"497","last_page":"502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9988999962806702,"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":0.9988999962806702,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9970999956130981,"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/planner","display_name":"Planner","score":0.8621395826339722},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7222188711166382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6441184282302856},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6118461489677429},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.555957019329071},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5548478364944458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.541799008846283},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5387834310531616},{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.5144777297973633},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.49256616830825806},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.4779358208179474},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.42856526374816895},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12850633263587952},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.08725389838218689}],"concepts":[{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.8621395826339722},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7222188711166382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6441184282302856},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6118461489677429},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.555957019329071},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5548478364944458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.541799008846283},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5387834310531616},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.5144777297973633},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.49256616830825806},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.4779358208179474},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.42856526374816895},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12850633263587952},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.08725389838218689},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6399999856948853,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G4318353608","display_name":"The realization of next-generation SLAM technique with incremental adaptation to changing domains: cross-domain map learning","funder_award_id":"17K00361","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5123141126","display_name":"The realization of next-generation SLAM technique based on self-diagnosis map: maintenance-free SLAM","funder_award_id":"20K12008","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6929521073","display_name":null,"funder_award_id":"26330297,17K00361","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1532257412","https://openalex.org/W1686810756","https://openalex.org/W1821462560","https://openalex.org/W1964629479","https://openalex.org/W1968870451","https://openalex.org/W1996622724","https://openalex.org/W2041620409","https://openalex.org/W2099430963","https://openalex.org/W2102621601","https://openalex.org/W2110405746","https://openalex.org/W2117228865","https://openalex.org/W2124386111","https://openalex.org/W2144824356","https://openalex.org/W2151900353","https://openalex.org/W2161969291","https://openalex.org/W2168359464","https://openalex.org/W2200124539","https://openalex.org/W2256388387","https://openalex.org/W2262703527","https://openalex.org/W2284029970","https://openalex.org/W2335829695","https://openalex.org/W2605111497","https://openalex.org/W2768890541","https://openalex.org/W2784853476","https://openalex.org/W2785901219","https://openalex.org/W2894411343","https://openalex.org/W2895392434","https://openalex.org/W2914921243","https://openalex.org/W2962958942","https://openalex.org/W2963139856","https://openalex.org/W2964288524","https://openalex.org/W2965621489","https://openalex.org/W2991479749","https://openalex.org/W3004414671","https://openalex.org/W3102617634","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6691895530","https://openalex.org/W6720111944","https://openalex.org/W6747585041","https://openalex.org/W6754690855"],"related_works":["https://openalex.org/W2123651102","https://openalex.org/W2953027552","https://openalex.org/W2287992105","https://openalex.org/W2570299561","https://openalex.org/W2392831491","https://openalex.org/W1568779110","https://openalex.org/W2117555338","https://openalex.org/W804484174","https://openalex.org/W2130339357","https://openalex.org/W2160491016"],"abstract_inverted_index":{"This":[0],"paper":[1,113],"addresses":[2],"the":[3,31,44,47,73,105,136,150,154,161,172,182,195,200],"problem":[4,142],"of":[5,15,58,181,208],"active":[6,137],"visual":[7,36],"place":[8],"recognition":[9],"(VPR)":[10],"from":[11,43],"a":[12,21,35,83,91,121,140,145,167],"novel":[13,92,122],"perspective":[14],"long-term":[16],"autonomy.":[17],"In":[18],"our":[19],"approach,":[20],"next-best-view":[22],"(NBV)":[23],"planner":[24,49,100,125],"plans":[25],"an":[26],"optimal":[27],"action-observation-sequence":[28],"to":[29,82,148,174],"maximize":[30],"expected":[32],"cost-performance":[33],"for":[34,79,130],"route":[37],"classification":[38],"task.":[39],"A":[40],"difficulty":[41],"arises":[42],"fact":[45],"that":[46,101,126,194],"NBV":[48,65,99,124],"is":[50,127,164,192],"trained":[51],"and":[52,62,70,75,143,211],"tested":[53],"in":[54,111,206],"different":[55],"domains":[56],"(times":[57],"day,":[59],"weather":[60],"conditions,":[61],"seasons).":[63],"Existing":[64],"methods":[66],"may":[67],"be":[68],"confused":[69],"deteriorated":[71],"by":[72,90,160],"domain-shifts,":[74],"require":[76,104],"significant":[77],"efforts":[78],"adapting":[80],"them":[81],"new":[84],"domain.":[85],"We":[86,119,134,178],"address":[87,149],"this":[88,112],"issue":[89],"deep":[93],"convolutional":[94],"neural":[95],"network":[96],"(DNN)":[97],"-based":[98],"does":[102],"not":[103],"adaptation":[106],"step.":[107],"Our":[108],"main":[109],"contributions":[110],"are":[114],"summarized":[115],"as":[116,139,166],"follows:":[117],"(1)":[118],"present":[120,144],"domain-invariant":[123,168],"specifically":[128],"tailored":[129],"DNN-based":[131],"VPR.":[132],"(2)":[133],"formulate":[135],"VPR":[138,188,205,209],"POMDP":[141],"feasible":[146],"solution":[147],"inherent":[151],"intractability.":[152],"Specifically,":[153],"probability":[155],"distribution":[156],"vector":[157],"(PDV)":[158],"output":[159],"available":[162],"DNN":[163],"used":[165],"observation":[169],"model":[170],"without":[171],"need":[173],"retrain":[175],"it.":[176],"(3)":[177],"verify":[179],"efficacy":[180],"proposed":[183,196],"approach":[184,197],"through":[185],"challenging":[186],"cross-season":[187],"experiments,":[189],"where":[190],"it":[191],"confirmed":[193],"clearly":[198],"outperforms":[199],"previous":[201],"single-view-based":[202],"or":[203],"multi-view-based":[204],"terms":[207],"accuracy":[210],"action-observation-cost.":[212]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
