{"id":"https://openalex.org/W7166536595","doi":"https://doi.org/10.48550/arxiv.2606.27729","title":"Learning 1-Bit LiDAR-based Localization with Auxiliary Objective","display_name":"Learning 1-Bit LiDAR-based Localization with Auxiliary Objective","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166536595","doi":"https://doi.org/10.48550/arxiv.2606.27729"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.27729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27729","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.27729","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048621901","display_name":"Ke Yin","orcid":"https://orcid.org/0000-0002-8534-216X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Kaijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139553997","display_name":"Zhiyuan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139561645","display_name":"Tian Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139616397","display_name":"Wentao Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Wentao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139585794","display_name":"Cheng-zhong Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Cheng-zhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139620572","display_name":"Hui Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Hui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.7649999856948853,"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":0.7649999856948853,"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":0.15199999511241913,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.024000000208616257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.6218000054359436},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6177999973297119},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5716000199317932},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5249000191688538},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4000000059604645},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3310000002384186},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3142000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6823999881744385},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6177999973297119},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6079000234603882},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5716000199317932},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3684000074863434},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3142000138759613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C2985963534","wikidata":"https://www.wikidata.org/wiki/Q7603704","display_name":"State information","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C37061001","wikidata":"https://www.wikidata.org/wiki/Q3531721","display_name":"Binary Independence Model","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.27729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27729","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.27729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27729","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4544852077960968,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"6-DoF":[0,106],"LiDAR-based":[1,96],"localization":[2,17,39,200],"is":[3,41],"a":[4,46,192],"fundamental":[5],"capability":[6],"for":[7,33,62,105,130,169,198],"autonomous":[8],"systems":[9],"operating":[10],"in":[11,148,179],"large-scale":[12,184],"outdoor":[13,185],"environments.":[14],"Many":[15],"deep-learning-based":[16],"methods":[18,57],"have":[19],"achieved":[20],"promising":[21],"performance":[22,84],"so":[23],"far.":[24],"However,":[25],"as":[26],"one":[27],"of":[28,49,114,119,195],"the":[29,38,50,76,99,112,117,120,149,154,170,175,196],"always-on":[30],"modules":[31],"competing":[32],"limited":[34,171],"on-board":[35],"computational":[36],"resources,":[37],"module":[40],"expected":[42],"to":[43],"consume":[44],"only":[45],"small":[47],"portion":[48],"overall":[51],"compute":[52],"budget.":[53],"Most":[54],"existing":[55],"learning-based":[56],"are":[58],"still":[59],"too":[60],"heavy":[61],"this":[63,87,91],"purpose.":[64],"In":[65,86],"contrast,":[66],"binary":[67,101,150],"neural":[68,102],"networks":[69],"(BNNs)":[70],"offer":[71],"an":[72,140],"appealing":[73],"solution,":[74],"but":[75],"1-bit":[77],"compression":[78],"causes":[79],"severe":[80],"information":[81,146,155],"loss":[82,156],"and":[83,174],"drop.":[85],"paper,":[88],"we":[89,110,138],"address":[90],"challenge":[92],"by":[93,158],"proposing":[94],"Binarized":[95],"Localization":[97],"(BiLoc),":[98],"first":[100],"network":[103],"framework":[104],"LiDAR":[107,186,199],"localization.":[108],"Specifically,":[109],"reinterpret":[111],"training":[113],"BNNs":[115],"from":[116],"perspective":[118],"information-bottleneck":[121],"principle,":[122],"aiming":[123],"at":[124],"retaining":[125],"minimal":[126],"yet":[127],"sufficient":[128],"representations":[129],"pose":[131],"estimation":[132],"while":[133],"suppressing":[134],"redundant":[135],"variations.":[136],"And":[137],"introduce":[139],"auxiliary":[141,161],"objective":[142,162],"that":[143,167,189],"adaptively":[144],"regulates":[145],"retention":[147],"encoder,":[151],"effectively":[152],"mitigating":[153],"caused":[157],"binarization.":[159],"This":[160],"provides":[163],"additional":[164],"optimization":[165],"signals":[166],"compensate":[168],"representational":[172],"capacity":[173],"gradient":[176],"mismatch":[177],"inherent":[178],"BNNs.":[180,202],"Extensive":[181],"experiments":[182],"on":[183],"datasets":[187],"demonstrate":[188],"BiLoc":[190],"establishes":[191],"new":[193],"state":[194],"art":[197],"with":[201]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-30T00:00:00"}