{"id":"https://openalex.org/W2414078574","doi":"https://doi.org/10.1109/icra.2016.7487333","title":"Probabilistic multi-sensor fusion based on signed distance functions","display_name":"Probabilistic multi-sensor fusion based on signed distance functions","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2414078574","doi":"https://doi.org/10.1109/icra.2016.7487333","mag":"2414078574"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2016.7487333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487333","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":"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/A5007330724","display_name":"Vincent Dietrich","orcid":"https://orcid.org/0000-0003-0568-9727"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Vincent Dietrich","raw_affiliation_strings":["Siemens Corporate Technology, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Siemens Corporate Technology, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013116360","display_name":"Dong Chen","orcid":"https://orcid.org/0000-0001-8118-3889"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dong Chen","raw_affiliation_strings":["Siemens Corporate Technology, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Siemens Corporate Technology, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090063396","display_name":"Kai M. Wurm","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kai M. Wurm","raw_affiliation_strings":["Siemens Corporate Technology, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Siemens Corporate Technology, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020197129","display_name":"Georg von Wichert","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Georg v. Wichert","raw_affiliation_strings":["Siemens Corporate Technology, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Siemens Corporate Technology, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037798029","display_name":"Philipp Ennen","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Philipp Ennen","raw_affiliation_strings":["IMA/ZLW & IfU, RWTH Aachen University, Aachen, Germany"],"affiliations":[{"raw_affiliation_string":"IMA/ZLW & IfU, RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5007330724"],"corresponding_institution_ids":["https://openalex.org/I1325886976"],"apc_list":null,"apc_paid":null,"fwci":3.5473,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.93721212,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"7","issue":null,"first_page":"1873","last_page":"1878"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9975000023841858,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9975000023841858,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9909999966621399,"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":0.9908999800682068,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.8193270564079285},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.779983401298523},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.7624404430389404},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6777287721633911},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6278917193412781},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.5737317800521851},{"id":"https://openalex.org/keywords/signed-distance-function","display_name":"Signed distance function","score":0.526168704032898},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.49100497364997864},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47874897718429565},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.4538414180278778},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3892923593521118},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38401347398757935},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34382760524749756},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1763216257095337},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.17081722617149353},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13903602957725525},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11242517828941345},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.0769186019897461}],"concepts":[{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.8193270564079285},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.779983401298523},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.7624404430389404},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6777287721633911},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6278917193412781},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.5737317800521851},{"id":"https://openalex.org/C71169176","wikidata":"https://www.wikidata.org/wiki/Q7512907","display_name":"Signed distance function","level":2,"score":0.526168704032898},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.49100497364997864},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47874897718429565},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.4538414180278778},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3892923593521118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38401347398757935},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34382760524749756},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1763216257095337},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.17081722617149353},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13903602957725525},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11242517828941345},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0769186019897461},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2016.7487333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487333","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"},{"id":"pmh:oai:publications.rwth-aachen.de:682080","is_oa":false,"landing_page_url":"https://publications.rwth-aachen.de/record/682080","pdf_url":null,"source":{"id":"https://openalex.org/S4306401033","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Piscataway : IEEE 1873-1878 (2016). doi:10.1109/ICRA.2016.7487333","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W31424620","https://openalex.org/W1559022906","https://openalex.org/W1746819321","https://openalex.org/W1987648924","https://openalex.org/W1987985833","https://openalex.org/W2008073424","https://openalex.org/W2009422376","https://openalex.org/W2013345945","https://openalex.org/W2016285927","https://openalex.org/W2099953968","https://openalex.org/W2128384169","https://openalex.org/W2130684472","https://openalex.org/W2133844819","https://openalex.org/W2152864241","https://openalex.org/W2154418813","https://openalex.org/W2229412420","https://openalex.org/W4211049957","https://openalex.org/W4233857083","https://openalex.org/W6601271567","https://openalex.org/W6629804754","https://openalex.org/W6652231383"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W4243305035","https://openalex.org/W1537496349","https://openalex.org/W2509844848","https://openalex.org/W4293868501"],"abstract_inverted_index":{"In":[0,83],"this":[1],"paper,":[2],"we":[3,86],"present":[4],"an":[5,44],"approach":[6,52,73,113],"for":[7],"the":[8,28,33,48,70,105,112],"probabilistic":[9],"fusion":[10,16],"of":[11,47,69,104],"3D":[12],"sensor":[13,81,95],"measurements.":[14],"Our":[15],"algorithm":[17],"is":[18,114],"based":[19],"on":[20,56],"truncated":[21],"signed":[22],"distance":[23],"functions.":[24],"It":[25],"explicitly":[26],"considers":[27],"measurement":[29],"noise":[30,123],"by":[31],"modeling":[32],"surface":[34,41],"using":[35,79],"random":[36],"variables.":[37],"Furthermore,":[38],"our":[39,84],"proposed":[40],"model":[42],"provides":[43,100],"explicit":[45],"estimation":[46],"spatial":[49],"uncertainty.":[50,106],"The":[51,72],"can":[53],"be":[54],"implemented":[55],"a":[57,61,101],"GPU":[58],"to":[59,116],"achieve":[60],"high":[62],"update":[63],"performance":[64],"and":[65,78,97],"enable":[66],"online":[67],"updates":[68],"model.":[71],"was":[74],"evaluated":[75],"in":[76],"simulation":[77],"real":[80],"data.":[82],"experiments,":[85],"confirmed":[87],"that":[88,98,111],"it":[89,99],"accurately":[90],"estimates":[91],"surfaces":[92],"from":[93,119],"noisy":[94],"data":[96],"corresponding":[102],"estimate":[103],"We":[107],"could":[108],"also":[109],"show":[110],"able":[115],"fuse":[117],"measurements":[118],"sensors":[120],"with":[121],"different":[122],"characteristics.":[124]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}