{"id":"https://openalex.org/W7163576583","doi":"https://doi.org/10.48550/arxiv.2606.04618","title":"BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM","display_name":"BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163576583","doi":"https://doi.org/10.48550/arxiv.2606.04618"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.04618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04618","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.04618","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137840546","display_name":"Thanh Nguyen Canh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Canh, Thanh Nguyen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137904284","display_name":"Haolan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haolan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137904516","display_name":"Xiem HoangVan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"HoangVan, Xiem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006199824","display_name":"Antonio Sgorbissa","orcid":"https://orcid.org/0000-0001-7789-4311"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sgorbissa, Antonio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137823971","display_name":"Nak Young Chong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chong, Nak Young","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":0.8705999851226807,"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":0.8705999851226807,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.027699999511241913,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.012900000438094139,"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/mixture-model","display_name":"Mixture model","score":0.5575000047683716},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5496000051498413},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5468999743461609},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.5332000255584717},{"id":"https://openalex.org/keywords/simultaneous-localization-and-mapping","display_name":"Simultaneous localization and mapping","score":0.5024999976158142},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4643000066280365},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4569999873638153},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.42340001463890076},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.41670000553131104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7159000039100647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6675000190734863},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5575000047683716},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5496000051498413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5468999743461609},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.5332000255584717},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.5024999976158142},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4643000066280365},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4569999873638153},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42340001463890076},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.41670000553131104},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.3790000081062317},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.36000001430511475},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C2983325608","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Data association","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32679998874664307},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32659998536109924},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2605000138282776}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.04618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04618","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.04618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04618","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5454849004745483,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"},{"score":0.45022228360176086,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Probabilistic":[0],"data":[1],"association":[2,21,65,97,136],"(PDA)":[3],"improves":[4,145],"semantic":[5,46,85,107,133,156,182],"SLAM":[6,47],"in":[7,109,170],"perceptually":[8],"aliased":[9],"scenes,":[10],"but":[11],"existing":[12,71],"methods":[13],"often":[14],"assume":[15],"a":[16,49,55,60,92,117,131,148,174],"fixed":[17],"landmark":[18,114],"set,":[19],"recompute":[20],"weights":[22,137],"as":[23,106,130],"the":[24,74,128,152,167],"map":[25],"grows,":[26],"or":[27],"rely":[28],"on":[29,173],"hand-tuned":[30],"null-hypothesis":[31],"weights.":[32],"To":[33],"address":[34],"these":[35],"limitations,":[36],"we":[37],"propose":[38],"\\textbf{BPDA-GMM},":[39],"an":[40,140],"online":[41],"Bayesian":[42],"PDA":[43],"framework":[44],"for":[45],"with":[48],"growing":[50],"object-level":[51],"map.":[52],"BPDA-GMM":[53],"uses":[54],"Dirichlet-process":[56],"prior":[57],"to":[58,80,127,161,187],"induce":[59],"Chinese":[61],"Restaurant":[62],"Process":[63],"(CRP)":[64],"model,":[66,120],"where":[67],"accumulated":[68],"evidence":[69],"favors":[70],"landmarks,":[72],"and":[73,101,121,172,185,190,197],"concentration":[75],"parameter":[76],"assigns":[77],"probability":[78],"mass":[79],"new":[81],"landmarks.":[82],"For":[83],"each":[84],"detection,":[86],"plausible":[87],"candidates":[88],"are":[89,99,104,138,199],"selected":[90],"by":[91],"joint":[93],"semantic-geometric":[94],"gate,":[95],"CRP-weighted":[96],"probabilities":[98],"computed,":[100],"object":[102],"landmarks":[103,163],"updated":[105],"Gaussians":[108],"closed":[110],"form.":[111],"The":[112],"resulting":[113],"set":[115],"forms":[116],"Gaussian":[118],"mixture":[119],"its":[122],"dominant":[123],"component":[124],"is":[125],"passed":[126],"back-end":[129,150],"max-mixture":[132],"factor.":[134],"When":[135],"inconclusive,":[139],"ambiguity-triggered":[141],"$\u03b1$-divergence":[142],"tempering":[143],"step":[144],"discrimination.":[146],"Finally,":[147],"decoupled":[149],"zeroes":[151],"pose":[153],"Jacobian":[154],"of":[155],"factors,":[157],"allowing":[158],"noisy":[159],"detections":[160],"refine":[162],"without":[164],"directly":[165],"perturbing":[166],"trajectory.":[168],"Experiments":[169],"simulation":[171],"real":[175],"indoor":[176],"dataset":[177],"demonstrate":[178],"improved":[179],"trajectory":[180],"accuracy,":[181],"mapping":[183],"quality,":[184],"robustness":[186],"perceptual":[188],"aliasing":[189],"classifier":[191],"errors":[192],"over":[193],"state-of-the-art":[194],"baselines.":[195],"Code":[196],"video":[198],"publicly":[200],"available":[201],"at":[202],"https://github.com/thanhnguyencanh/BPDA-SLAM.":[203]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-05T00:00:00"}