{"id":"https://openalex.org/W2973181288","doi":"https://doi.org/10.1109/lra.2020.2965390","title":"Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping","display_name":"Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping","publication_year":2020,"publication_date":"2020-01-09","ids":{"openalex":"https://openalex.org/W2973181288","doi":"https://doi.org/10.1109/lra.2020.2965390","mag":"2973181288"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2020.2965390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2020.2965390","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.04631","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lu Gan","orcid":"https://orcid.org/0000-0003-0911-8032"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lu Gan","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"raw_orcid":"https://orcid.org/0000-0003-0911-8032","affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ray Zhang","orcid":"https://orcid.org/0000-0001-9599-931X"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ray Zhang","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"raw_orcid":"https://orcid.org/0000-0001-9599-931X","affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jessy W. Grizzle","orcid":"https://orcid.org/0000-0001-7586-0142"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jessy W. Grizzle","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"raw_orcid":"https://orcid.org/0000-0001-7586-0142","affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ryan M. Eustice","orcid":"https://orcid.org/0000-0002-9989-4942"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan M. Eustice","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"raw_orcid":"https://orcid.org/0000-0002-9989-4942","affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":null,"display_name":"Maani Ghaffari","orcid":"https://orcid.org/0000-0002-4734-4295"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maani Ghaffari","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"raw_orcid":"https://orcid.org/0000-0002-4734-4295","affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":70.0201,"has_fulltext":false,"cited_by_count":71,"citation_normalized_percentile":{"value":0.9980901,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"5","issue":"2","first_page":"790","last_page":"797"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.8597000241279602,"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":0.8597000241279602,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.03449999913573265,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.027400000020861626,"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/occupancy-grid-mapping","display_name":"Occupancy grid mapping","score":0.7257999777793884},{"id":"https://openalex.org/keywords/semantic-mapping","display_name":"Semantic mapping","score":0.5343000292778015},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5264000296592712},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.49149999022483826},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4699000120162964},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.46700000762939453},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4242999851703644},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4162999987602234},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.397599995136261}],"concepts":[{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.7257999777793884},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764000058174133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5867999792098999},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5264000296592712},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.49149999022483826},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4699000120162964},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.46700000762939453},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3855000138282776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36239999532699585},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33059999346733093},{"id":"https://openalex.org/C27406209","wikidata":"https://www.wikidata.org/wiki/Q6394203","display_name":"Kernel smoother","level":5,"score":0.3206999897956848},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.2718999981880188},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C156172958","wikidata":"https://www.wikidata.org/wiki/Q3438407","display_name":"Grid reference","level":4,"score":0.2556999921798706}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lra.2020.2965390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2020.2965390","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1909.04631","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.04631","pdf_url":"https://arxiv.org/pdf/1909.04631","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1909.04631","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.04631","pdf_url":"https://arxiv.org/pdf/1909.04631","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7550594291","display_name":null,"funder_award_id":"N021515","funder_id":"https://openalex.org/F4320315934","funder_display_name":"Toyota Research Institute"}],"funders":[{"id":"https://openalex.org/F4320315934","display_name":"Toyota Research Institute","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1572232541","https://openalex.org/W1595654559","https://openalex.org/W1612997784","https://openalex.org/W1964502729","https://openalex.org/W1971618559","https://openalex.org/W1977189000","https://openalex.org/W2000063120","https://openalex.org/W2013229266","https://openalex.org/W2028284985","https://openalex.org/W2045587041","https://openalex.org/W2108598243","https://openalex.org/W2118246710","https://openalex.org/W2130684472","https://openalex.org/W2133844819","https://openalex.org/W2136097307","https://openalex.org/W2142879048","https://openalex.org/W2152864241","https://openalex.org/W2154418813","https://openalex.org/W2158057652","https://openalex.org/W2167687475","https://openalex.org/W2171481071","https://openalex.org/W2265661972","https://openalex.org/W2335829695","https://openalex.org/W2346044757","https://openalex.org/W2410641873","https://openalex.org/W2412342236","https://openalex.org/W2470820246","https://openalex.org/W2496015086","https://openalex.org/W2523049145","https://openalex.org/W2569527682","https://openalex.org/W2737856893","https://openalex.org/W2738569225","https://openalex.org/W2804892083","https://openalex.org/W2805521962","https://openalex.org/W2945883617","https://openalex.org/W2963632154","https://openalex.org/W2963954267","https://openalex.org/W6603706610","https://openalex.org/W6622862201","https://openalex.org/W6640185247","https://openalex.org/W6696085341","https://openalex.org/W6733367512","https://openalex.org/W6740277193","https://openalex.org/W6745072397","https://openalex.org/W6755791307","https://openalex.org/W6756486208","https://openalex.org/W6766771393","https://openalex.org/W6773342343","https://openalex.org/W6795640044"],"related_works":[],"abstract_inverted_index":{"This":[0],"article":[1],"develops":[2],"a":[3,25,31,38,112,137,144],"Bayesian":[4,17,67],"continuous":[5],"3D":[6],"semantic":[7,29,46],"occupancy":[8,23,44,54],"map":[9,83],"from":[10],"noisy":[11],"point":[12],"clouds":[13],"by":[14],"generalizing":[15],"the":[16,52,72,82,92,96,127,149],"kernel":[18,69],"inference":[19,70],"model":[20,41],"for":[21,42],"building":[22],"maps,":[24,30],"binary":[26],"problem,":[27],"to":[28,51,81,86],"multi-class":[32],"problem.":[33],"The":[34,66,98],"proposed":[35,128],"method":[36,129],"provides":[37],"unified":[39],"probabilistic":[40],"both":[43],"and":[45,48,76,79,94,103,105,122],"probabilities":[47],"nicely":[49],"reverts":[50],"original":[53],"mapping":[55],"framework":[56],"when":[57],"only":[58],"one":[59],"occupied":[60],"class":[61],"exists":[62],"in":[63,91],"obtained":[64],"measurements.":[65],"spatial":[68],"relaxes":[71],"independent":[73],"grid":[74],"assumption":[75],"brings":[77],"smoothness":[78],"continuity":[80],"inference,":[84],"enabling":[85],"exploit":[87],"local":[88],"correlations":[89],"present":[90,136],"environment":[93],"increasing":[95],"performance.":[97],"accompanying":[99],"software":[100],"uses":[101],"multi-threading":[102],"vectorization,":[104],"runs":[106],"at":[107],"about":[108],"2":[109],"Hz":[110],"on":[111,148],"laptop":[113],"CPU.":[114],"Evaluations":[115],"using":[116,140],"multiple":[117],"sequences":[118],"of":[119,151],"stereo":[120],"camera":[121],"LiDAR":[123],"datasets":[124],"show":[125],"that":[126],"consistently":[130],"outperforms":[131],"current":[132],"baselines.":[133],"We":[134],"also":[135],"qualitative":[138],"evaluation":[139],"data":[141],"collected":[142],"with":[143],"bipedal":[145],"robot":[146],"platform":[147],"University":[150],"Michigan":[152],"-":[153],"North":[154],"Campus.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2019-09-19T00:00:00"}
