{"id":"https://openalex.org/W2157380963","doi":"https://doi.org/10.1109/itsc.2009.5309555","title":"Robust ground plane estimation based on particle filters","display_name":"Robust ground plane estimation based on particle filters","publication_year":2009,"publication_date":"2009-10-01","ids":{"openalex":"https://openalex.org/W2157380963","doi":"https://doi.org/10.1109/itsc.2009.5309555","mag":"2157380963"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2009.5309555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2009.5309555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 12th International IEEE Conference on Intelligent Transportation Systems","raw_type":"proceedings-article"},"type":"article","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/A5023054248","display_name":"A. Linarth","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Andre G. Linarth","raw_affiliation_strings":["Sensor Information, Elektrobit Automotive GmbH, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Sensor Information, Elektrobit Automotive GmbH, Erlangen, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065872890","display_name":"Manuel Brucker","orcid":"https://orcid.org/0000-0001-6370-2753"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Manuel Brucker","raw_affiliation_strings":["Pattern Recognition, Friedrich-Alexander-Universitat Erlangen-Nuremberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition, Friedrich-Alexander-Universitat Erlangen-Nuremberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049805682","display_name":"Elli Angelopoulou","orcid":null},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Elli Angelopoulou","raw_affiliation_strings":["Pattern Recognition, Friedrich-Alexander-Universitat Erlangen-Nuremberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition, Friedrich-Alexander-Universitat Erlangen-Nuremberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023054248"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.12332924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9987000226974487,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.8323702812194824},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.6382394433021545},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5819342732429504},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.5622689723968506},{"id":"https://openalex.org/keywords/plane","display_name":"Plane (geometry)","score":0.4931785464286804},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4864911437034607},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47182852029800415},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.45587486028671265},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4392206370830536},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3613203167915344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2917361855506897},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2802080512046814},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.26107585430145264},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08352223038673401}],"concepts":[{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.8323702812194824},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.6382394433021545},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5819342732429504},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.5622689723968506},{"id":"https://openalex.org/C17825722","wikidata":"https://www.wikidata.org/wiki/Q17285","display_name":"Plane (geometry)","level":2,"score":0.4931785464286804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4864911437034607},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47182852029800415},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.45587486028671265},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4392206370830536},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3613203167915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2917361855506897},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2802080512046814},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26107585430145264},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08352223038673401},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2009.5309555","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2009.5309555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 12th International IEEE Conference on Intelligent Transportation Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1233635381","https://openalex.org/W1565206031","https://openalex.org/W1964344553","https://openalex.org/W2024763827","https://openalex.org/W2033819227","https://openalex.org/W2035379092","https://openalex.org/W2055936398","https://openalex.org/W2067191022","https://openalex.org/W2096204758","https://openalex.org/W2111308925","https://openalex.org/W2117658559","https://openalex.org/W2124150409","https://openalex.org/W2138430167","https://openalex.org/W2144297066","https://openalex.org/W2147122541","https://openalex.org/W2159081551","https://openalex.org/W2160337655","https://openalex.org/W2161350794","https://openalex.org/W6628170318"],"related_works":["https://openalex.org/W2015530857","https://openalex.org/W118630527","https://openalex.org/W2556064263","https://openalex.org/W38289631","https://openalex.org/W4252337044","https://openalex.org/W1991846142","https://openalex.org/W1583020711","https://openalex.org/W1589286931","https://openalex.org/W2592886773","https://openalex.org/W1975715045"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"a":[3,19,26,40,65,117],"particle":[4,23],"filter":[5,24],"based":[6],"approach":[7],"for":[8,43],"estimating":[9],"the":[10,22,30,48,51,55,73,76,109],"ground":[11],"plane":[12,31],"from":[13],"an":[14],"image":[15],"sequence.":[16],"Based":[17],"on":[18,85],"Bayesian":[20],"framework,":[21],"provides":[25],"robust":[27,97],"estimation":[28],"of":[29,54,64,72,106,120],"parameters,":[32],"since":[33],"it":[34],"can":[35],"handle":[36],"non-linearities,":[37],"while":[38],"allowing":[39],"high":[41],"flexibility":[42],"integrating":[44],"new":[45],"cues":[46],"into":[47],"system.":[49],"Furthermore,":[50],"different":[52],"modes":[53],"resulting":[56,68],"probability":[57],"density":[58],"function":[59],"are":[60],"segmented":[61],"by":[62],"means":[63],"mean-shift":[66],"algorithm,":[67],"in":[69],"better":[70],"localization":[71],"estimate":[74],"with":[75,116],"highest":[77],"posterior":[78],"probability.":[79],"Our":[80],"method":[81],"has":[82,93],"been":[83],"tested":[84],"both":[86],"synthetic":[87,104],"and":[88,92,100],"real":[89],"world":[90],"scenarios":[91],"shown":[94],"to":[95,98],"be":[96],"missing":[99],"unstable":[101],"measurements.":[102],"On":[103],"data":[105],"representative":[107],"runs":[108],"angular":[110],"error":[111],"is":[112],"well":[113],"within":[114],"0.5deg":[115],"standard":[118],"deviation":[119],"less":[121],"than":[122],"0.3deg.":[123]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}