{"id":"https://openalex.org/W4416702642","doi":"https://doi.org/10.48550/arxiv.2509.01968","title":"Ensemble-Based Event Camera Place Recognition Under Varying Illumination","display_name":"Ensemble-Based Event Camera Place Recognition Under Varying Illumination","publication_year":2025,"publication_date":"2025-09-02","ids":{"openalex":"https://openalex.org/W4416702642","doi":"https://doi.org/10.48550/arxiv.2509.01968"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.01968","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.01968","pdf_url":"https://arxiv.org/pdf/2509.01968","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.01968","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079836536","display_name":"Therese Joseph","orcid":"https://orcid.org/0000-0002-4789-7009"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Joseph, Therese","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071424922","display_name":"Tobias Fischer","orcid":"https://orcid.org/0000-0003-2183-017X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fischer, Tobias","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5078340555","display_name":"Michael Milford","orcid":"https://orcid.org/0000-0002-5162-1793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Milford, Michael","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079836536"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9753000140190125,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9753000140190125,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.002099999925121665,"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/T13553","display_name":"Age of Information Optimization","score":0.0020000000949949026,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7946000099182129},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5026999711990356},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.45829999446868896},{"id":"https://openalex.org/keywords/codebase","display_name":"Codebase","score":0.4560000002384186},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4514999985694885},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.44209998846054077},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.38679999113082886},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.37689998745918274},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.3540000021457672},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3221000134944916}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7946000099182129},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276999950408936},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7035999894142151},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6549999713897705},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5026999711990356},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C51929080","wikidata":"https://www.wikidata.org/wiki/Q2425187","display_name":"Codebase","level":3,"score":0.4560000002384186},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.30329999327659607},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2583000063896179},{"id":"https://openalex.org/C38785706","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Interest point detection","level":5,"score":0.25769999623298645},{"id":"https://openalex.org/C30684385","wikidata":"https://www.wikidata.org/wiki/Q176509","display_name":"Ringing","level":3,"score":0.2565000057220459},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.01968","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.01968","pdf_url":"https://arxiv.org/pdf/2509.01968","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"doi:10.48550/arxiv.2509.01968","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.01968","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.01968","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.01968","pdf_url":"https://arxiv.org/pdf/2509.01968","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Compared":[0],"to":[1,17,59,164,178],"conventional":[2],"cameras,":[3],"event":[4,28,60,142],"cameras":[5,29],"provide":[6],"a":[7,105,147,176],"high":[8],"dynamic":[9],"range":[10],"and":[11,20,75,137,161,200],"low":[12],"latency,":[13],"offering":[14],"greater":[15],"robustness":[16,95],"rapid":[18],"motion":[19],"challenging":[21],"lighting":[22,98],"conditions.":[23],"Although":[24],"the":[25,166,179],"potential":[26],"of":[27,150],"for":[30,170],"visual":[31],"place":[32,62],"recognition":[33,63],"(VPR)":[34],"has":[35],"been":[36],"established,":[37],"developing":[38],"robust":[39,171],"VPR":[40,72],"frameworks":[41],"under":[42,96],"severe":[43],"illumination":[44],"changes":[45],"remains":[46],"an":[47,56],"open":[48],"research":[49],"problem.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54,174,195],"introduce":[55],"ensemble-based":[57],"approach":[58,117],"camera":[61],"that":[64,140,184],"combines":[65],"sequence-matched":[66],"results":[67],"from":[68],"multiple":[69],"event-to-frame":[70],"reconstructions,":[71],"feature":[73,162],"extractors,":[74,163],"temporal":[76,86],"resolutions.":[77],"Unlike":[78],"previous":[79],"event-based":[80],"ensemble":[81],"methods,":[82,160],"which":[83],"only":[84],"utilise":[85],"resolution,":[87],"our":[88,116,198],"broader":[89],"fusion":[90],"strategy":[91],"delivers":[92],"significantly":[93],"improved":[94],"varied":[97],"conditions":[99],"(e.g.,":[100],"afternoon,":[101],"sunset,":[102],"night),":[103],"achieving":[104],"57%":[106],"relative":[107],"improvement":[108],"in":[109,135],"Recall@1":[110],"across":[111],"day-night":[112],"transitions.":[113],"We":[114,144],"evaluate":[115],"on":[118],"two":[119],"long-term":[120],"driving":[121],"datasets":[122],"(with":[123],"8":[124],"km":[125],"per":[126],"traverse)":[127],"without":[128],"metric":[129],"subsampling,":[130],"thereby":[131],"preserving":[132],"natural":[133],"variations":[134],"speed":[136],"stop":[138],"duration":[139],"influence":[141],"density.":[143],"also":[145],"conduct":[146],"comprehensive":[148],"analysis":[149],"key":[151],"design":[152],"choices,":[153],"including":[154],"binning":[155],"strategies,":[156],"polarity":[157],"handling,":[158],"reconstruction":[159],"identify":[165],"most":[167],"critical":[168],"components":[169],"performance.":[172],"Additionally,":[173],"propose":[175],"modification":[177],"standard":[180],"sequence":[181,189],"matching":[182],"framework":[183],"enhances":[185],"performance":[186],"at":[187],"longer":[188],"lengths.":[190],"To":[191],"facilitate":[192],"future":[193],"research,":[194],"will":[196],"release":[197],"codebase":[199],"benchmarking":[201],"framework.":[202]},"counts_by_year":[],"updated_date":"2026-03-16T09:10:04.655348","created_date":"2025-10-10T00:00:00"}