{"id":"https://openalex.org/W2410641873","doi":"https://doi.org/10.1109/icra.2016.7487232","title":"Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion","display_name":"Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2410641873","doi":"https://doi.org/10.1109/icra.2016.7487232","mag":"2410641873"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2016.7487232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090819950","display_name":"Jinkun Wang","orcid":"https://orcid.org/0000-0001-7030-5035"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinkun Wang","raw_affiliation_strings":["Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044251670","display_name":"Brendan Englot","orcid":"https://orcid.org/0000-0002-7966-2917"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brendan Englot","raw_affiliation_strings":["Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":null,"apc_paid":null,"fwci":4.8532,"has_fulltext":false,"cited_by_count":82,"citation_normalized_percentile":{"value":0.965638,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1003","last_page":"1010"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9943000078201294,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.7877790927886963},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5923002362251282},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5254610776901245},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5168771743774414},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.5104809999465942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47203031182289124},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43038955330848694},{"id":"https://openalex.org/keywords/quadtree","display_name":"Quadtree","score":0.41180622577667236},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37612059712409973},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.32537609338760376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7877790927886963},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5923002362251282},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5254610776901245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5168771743774414},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.5104809999465942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47203031182289124},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43038955330848694},{"id":"https://openalex.org/C151416825","wikidata":"https://www.wikidata.org/wiki/Q934791","display_name":"Quadtree","level":2,"score":0.41180622577667236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37612059712409973},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.32537609338760376},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2016.7487232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W55912154","https://openalex.org/W91269655","https://openalex.org/W1973310094","https://openalex.org/W1977189000","https://openalex.org/W1988888548","https://openalex.org/W1991752086","https://openalex.org/W1996985406","https://openalex.org/W1999050017","https://openalex.org/W2013229266","https://openalex.org/W2037907772","https://openalex.org/W2082300941","https://openalex.org/W2108966602","https://openalex.org/W2115519811","https://openalex.org/W2124825847","https://openalex.org/W2129298951","https://openalex.org/W2130684472","https://openalex.org/W2133844819","https://openalex.org/W2152864241","https://openalex.org/W2161884391","https://openalex.org/W2167340365","https://openalex.org/W2168464387","https://openalex.org/W2212202518","https://openalex.org/W2291737362","https://openalex.org/W2901136733","https://openalex.org/W2963896465","https://openalex.org/W3083135905","https://openalex.org/W4211049957","https://openalex.org/W6603706610","https://openalex.org/W6638703680","https://openalex.org/W6660115907","https://openalex.org/W6676276870","https://openalex.org/W6677508557","https://openalex.org/W6679975623","https://openalex.org/W6684209365","https://openalex.org/W6688517921","https://openalex.org/W6756486208"],"related_works":["https://openalex.org/W2095430756","https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W4293503520","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655"],"abstract_inverted_index":{"We":[0,103],"present":[1],"a":[2,94,106,146,202],"novel":[3],"algorithm":[4],"to":[5,28,54,98,159,205],"produce":[6],"descriptive":[7],"online":[8,58],"3D":[9],"occupancy":[10],"maps":[11],"using":[12],"Gaussian":[13],"processes":[14],"(GPs).":[15],"GP":[16,120],"regression":[17],"and":[18,42,57,125,144,169,186,200],"classification":[19,154,173],"have":[20],"met":[21],"with":[22,129,172,183],"recent":[23],"success":[24],"in":[25,93,96,150],"their":[26],"application":[27,53],"robot":[29],"mapping,":[30],"as":[31,196,201],"GPs":[32,167],"are":[33,157],"capable":[34],"of":[35,75,82,89,138,140,163,177],"expressing":[36],"rich":[37],"correlation":[38],"among":[39,118],"map":[40,77,128],"cells":[41],"sensor":[43,114],"data.":[44],"However,":[45],"the":[46,76,83,87,123,127,136,141,161,178,191],"cubic":[47],"computational":[48],"complexity":[49],"has":[50],"limited":[51],"its":[52,170],"large-scale":[55],"mapping":[56],"use.":[59],"In":[60],"this":[61,65],"paper":[62],"we":[63,156],"address":[64],"issue":[66],"first":[67],"by":[68,134,166],"proposing":[69],"test-data":[70],"octrees,":[71],"octrees":[72],"within":[73],"blocks":[74],"that":[78,190],"prune":[79],"away":[80],"nodes":[81],"same":[84],"state,":[85],"condensing":[86],"number":[88],"test":[90],"data":[91,101,115,143],"used":[92],"regression,":[95],"addition":[97],"allowing":[99],"fast":[100],"retrieval.":[102],"also":[104],"propose":[105],"nested":[107],"Bayesian":[108],"committee":[109],"machine":[110],"which,":[111],"after":[112],"new":[113],"is":[116,181],"partitioned":[117],"several":[119],"regressions,":[121],"fuses":[122],"result":[124],"updates":[126],"greatly":[130],"reduced":[131],"complexity.":[132],"Finally,":[133],"adjusting":[135],"range":[137],"influence":[139],"training":[142],"tuning":[145],"variance":[147],"threshold":[148],"implemented":[149],"our":[151],"method's":[152],"binary":[153],"step,":[155],"able":[158],"control":[160],"richness":[162],"inference":[164],"achieved":[165],"-":[168],"tradeoff":[171],"accuracy.":[174],"The":[175],"performance":[176],"proposed":[179],"approach":[180],"evaluated":[182],"both":[184,195],"simulated":[185],"real":[187],"data,":[188],"demonstrating":[189],"method":[192],"may":[193],"serve":[194],"an":[197],"improved-accuracy":[198],"classifier,":[199],"predictive":[203],"tool":[204],"support":[206],"autonomous":[207],"navigation.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":10}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
