{"id":"https://openalex.org/W7133347019","doi":"https://doi.org/10.48550/arxiv.2603.01673","title":"B$^2$F-Map: Crowd-sourced Mapping with Bayesian B-spline Fusion","display_name":"B$^2$F-Map: Crowd-sourced Mapping with Bayesian B-spline Fusion","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133347019","doi":"https://doi.org/10.48550/arxiv.2603.01673"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01673","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01673","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.2603.01673","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127926975","display_name":"Yiping Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Yiping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059294871","display_name":"Yuxuan Xia","orcid":"https://orcid.org/0009-0009-5265-6531"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000568479","display_name":"Erik Stenborg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stenborg, Erik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127971845","display_name":"Junsheng Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Junsheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054567051","display_name":"Axel Beauvisage","orcid":"https://orcid.org/0000-0002-4977-9743"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beauvisage, Axel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127911232","display_name":"Gabriel Gaspar Garcia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garcia, Gabriel E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127897429","display_name":"Tianyu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Tianyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5001137240","display_name":"Gustaf Hendeby","orcid":"https://orcid.org/0000-0002-1971-4295"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hendeby, Gustaf","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":0.5228000283241272,"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":0.5228000283241272,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.26100000739097595,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.04670000076293945,"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/bayesian-probability","display_name":"Bayesian probability","score":0.5623999834060669},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5273000001907349},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5026000142097473},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4413999915122986},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.42820000648498535},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4081999957561493},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.40470001101493835},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.39100000262260437},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3898000121116638},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38269999623298645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6355000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6238999962806702},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5623999834060669},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5273000001907349},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46219998598098755},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4081999957561493},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.35830000042915344},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C2983325608","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Data association","level":3,"score":0.34540000557899475},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.334199994802475},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C2779679900","wikidata":"https://www.wikidata.org/wiki/Q25304431","display_name":"Saliency map","level":3,"score":0.32519999146461487},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.28839999437332153},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.27810001373291016},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26179999113082886},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01673","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01673","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.2603.01673","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01673","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Crowd-sourced":[0],"mapping":[1,65],"offers":[2],"a":[3,35,48,100,109],"scalable":[4],"alternative":[5],"to":[6,148],"creating":[7],"maps":[8,22],"using":[9,42,60,92],"traditional":[10],"survey":[11],"vehicles.":[12],"Yet,":[13],"existing":[14],"methods":[15],"either":[16],"rely":[17],"on":[18,132],"prior":[19],"high-definition":[20],"(HD)":[21],"or":[23],"neglect":[24],"uncertainties":[25],"in":[26],"the":[27,89],"map":[28,40,85],"fusion.":[29,86],"In":[30],"this":[31],"work,":[32],"we":[33],"present":[34],"complete":[36],"pipeline":[37],"for":[38,114],"HD":[39],"generation":[41],"production":[43],"vehicles":[44],"equipped":[45],"only":[46],"with":[47,76,117],"monocular":[49],"camera,":[50],"consumer-grade":[51],"GNSS,":[52],"and":[53,79,83,107,141],"IMU.":[54],"Our":[55],"approach":[56],"includes":[57],"on-cloud":[58,80],"localization":[59],"lightweight":[61],"standard-definition":[62],"maps,":[63],"on-vehicle":[64],"via":[66],"an":[67],"extended":[68],"object":[69],"trajectory":[70],"(EOT)":[71],"Poisson":[72],"multi-Bernoulli":[73],"(PMB)":[74],"filter":[75],"Gibbs":[77],"sampling,":[78],"multi-drive":[81],"optimization":[82],"Bayesian":[84,111],"We":[87,126],"represent":[88],"lane":[90],"lines":[91],"B-splines,":[93],"where":[94],"each":[95],"B-spline":[96,115],"is":[97,146],"parameterized":[98],"by":[99],"sequence":[101],"of":[102,124],"Gaussian":[103],"distributed":[104],"control":[105],"points,":[106],"propose":[108],"novel":[110],"fusion":[112],"framework":[113],"trajectories":[116],"differing":[118],"density":[119],"representation,":[120],"enabling":[121],"principled":[122],"handling":[123],"uncertainties.":[125],"evaluate":[127],"our":[128,144],"proposed":[129],"approach,":[130],"B$^2$F-Map,":[131],"large-scale":[133],"real-world":[134],"datasets":[135],"collected":[136],"across":[137],"diverse":[138],"driving":[139],"conditions":[140],"demonstrate":[142],"that":[143],"method":[145],"able":[147],"produce":[149],"geometrically":[150],"consistent":[151],"lane-level":[152],"maps.":[153]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}
