{"id":"https://openalex.org/W7139101285","doi":"https://doi.org/10.48550/arxiv.2603.16273","title":"GenZ-LIO: Generalizable LiDAR-Inertial Odometry Beyond Confined--Open Boundaries","display_name":"GenZ-LIO: Generalizable LiDAR-Inertial Odometry Beyond Confined--Open Boundaries","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7139101285","doi":"https://doi.org/10.48550/arxiv.2603.16273"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.16273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16273","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.16273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129979877","display_name":"Daehan Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Daehan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077817769","display_name":"Hyungtae Lim","orcid":"https://orcid.org/0000-0002-7185-4666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Hyungtae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130200755","display_name":"Seongjun Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Seongjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129821336","display_name":"Soonbin Rho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rho, Soonbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129810283","display_name":"Changhyeon Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Changhyeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130101455","display_name":"Sanghyun Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Sanghyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059534323","display_name":"Junwoo Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Junwoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121492185","display_name":"Eunseon Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Eunseon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126655788","display_name":"Hyunyoung Jo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jo, Hyunyoung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129861962","display_name":"Soohee Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Soohee","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":0.9552000164985657,"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":0.9552000164985657,"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":0.007699999958276749,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.007300000172108412,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.7008000016212463},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6617000102996826},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5565999746322632},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5443999767303467},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.47099998593330383},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43549999594688416},{"id":"https://openalex.org/keywords/simultaneous-localization-and-mapping","display_name":"Simultaneous localization and mapping","score":0.4203000068664551},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.4129999876022339},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.39070001244544983}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7501999735832214},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.710099995136261},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.7008000016212463},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6782000064849854},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6617000102996826},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5565999746322632},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5443999767303467},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.47099998593330383},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.4203000068664551},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.4129999876022339},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.36899998784065247},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.3246000111103058},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.31209999322891235},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C2780502288","wikidata":"https://www.wikidata.org/wiki/Q28838156","display_name":"Expansive","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C31487907","wikidata":"https://www.wikidata.org/wiki/Q1154597","display_name":"Polygon mesh","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.2700999975204468},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25850000977516174},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.2508000135421753},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.16273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16273","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.16273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16273","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"For":[0],"field":[1,47,160,179],"robotic":[2],"missions":[3],"such":[4],"as":[5,19],"inspection,":[6],"search-and-rescue,":[7],"and":[8,12,28,39,56,64,90,116,123,144,184],"exploration,":[9],"light":[10],"detection":[11],"ranging":[13],"(LiDAR)-inertial":[14],"odometry":[15,169],"(LIO)":[16],"can":[17,49],"serve":[18],"a":[20,76,134],"core":[21],"component":[22],"of":[23,67],"autonomy":[24],"by":[25],"providing":[26],"localization":[27],"mapping":[29],"in":[30,46,53,85],"GNSS-denied":[31],"or":[32],"unstructured":[33],"environments.":[34,92],"However,":[35],"transitions":[36],"between":[37],"confined":[38,89],"open":[40,91],"spaces,":[41],"which":[42],"are":[43],"commonly":[44],"encountered":[45],"deployments,":[48],"induce":[50],"substantial":[51],"changes":[52],"scan":[54,103],"density":[55],"local":[57],"geometric":[58,121],"structure,":[59,122],"thereby":[60],"reducing":[61],"the":[62,163,177],"robustness":[63,175],"computational":[65],"efficiency":[66],"LIO.":[68],"To":[69],"address":[70],"these":[71],"issues,":[72],"we":[73],"present":[74],"GenZ-LIO,":[75],"generalizable":[77],"LIO":[78,152],"framework":[79],"designed":[80],"to":[81,83,150],"adapt":[82],"variations":[84,157],"spatial":[86,106,155],"scale":[87,107,156],"across":[88,105,158],"GenZ-LIO":[93,166],"comprises":[94],"three":[95],"components:":[96],"(i)":[97],"scale-aware":[98],"adaptive":[99],"voxelization":[100],"for":[101,113,128],"regulating":[102],"downsampling":[104],"changes,":[108],"(ii)":[109],"hybrid-metric":[110],"state":[111],"update":[112],"combining":[114],"point-to-plane":[115],"point-to-point":[117,130],"residuals":[118],"under":[119,154,176],"varying":[120],"(iii)":[124],"voxel-pruned":[125],"correspondence":[126],"search":[127],"efficient":[129],"matching.":[131],"We":[132],"conduct":[133],"comprehensive":[135],"evaluation":[136],"using":[137],"42":[138],"sequences":[139],"from":[140],"nine":[141],"public":[142],"datasets":[143],"our":[145],"newly":[146],"collected":[147,185],"NarrowWide":[148],"dataset":[149,186],"analyze":[151],"performance":[153],"diverse":[159],"scenarios.":[161],"Across":[162],"evaluated":[164],"sequences,":[165],"maintains":[167],"stable":[168],"estimation":[170],"without":[171],"divergence,":[172],"indicating":[173],"practical":[174],"tested":[178],"conditions.":[180],"The":[181],"source":[182],"code":[183],"will":[187],"be":[188],"made":[189],"publicly":[190],"available":[191],"upon":[192],"publication.":[193]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-20T00:00:00"}
