{"id":"https://openalex.org/W2970981142","doi":"https://doi.org/10.1109/ivs.2019.8813840","title":"Learning Scene Adaptive Covariance Error Model of LiDAR Scan Matching for Fusion Based Localization","display_name":"Learning Scene Adaptive Covariance Error Model of LiDAR Scan Matching for Fusion Based Localization","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2970981142","doi":"https://doi.org/10.1109/ivs.2019.8813840","mag":"2970981142"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2019.8813840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8813840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","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/A5037835763","display_name":"Xiaoliang Ju","orcid":"https://orcid.org/0000-0003-3875-6244"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoliang Ju","raw_affiliation_strings":["Peking University, Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Beijing, 100871, China","Peking University, School of Electronics Engineering and Computer Science, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, School of Electronics Engineering and Computer Science, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102997561","display_name":"Donghao Xu","orcid":"https://orcid.org/0000-0003-0568-8740"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donghao Xu","raw_affiliation_strings":["Peking University, Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Beijing, 100871, China","Peking University, School of Electronics Engineering and Computer Science, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, School of Electronics Engineering and Computer Science, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002980949","display_name":"Xijun Zhao","orcid":"https://orcid.org/0000-0003-2090-3648"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xijun Zhao","raw_affiliation_strings":["China North Vehicle Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China North Vehicle Research Institute, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089866078","display_name":"Wen Yao","orcid":"https://orcid.org/0000-0001-5224-9834"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen Yao","raw_affiliation_strings":["China North Vehicle Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China North Vehicle Research Institute, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060007938","display_name":"Huijing Zhao","orcid":"https://orcid.org/0000-0001-9245-3039"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huijing Zhao","raw_affiliation_strings":["Peking University, Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Beijing, 100871, China","Peking University, School of Electronics Engineering and Computer Science, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, School of Electronics Engineering and Computer Science, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1789","last_page":"1796"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":1.0,"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":1.0,"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":0.9969000220298767,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7775369882583618},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7632892727851868},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.7366330623626709},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7219670414924622},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6575233936309814},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6017158031463623},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5278598666191101},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5163275599479675},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.47749847173690796},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4015599191188812},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20315426588058472},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.19202792644500732},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1257152557373047},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09077325463294983}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775369882583618},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7632892727851868},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.7366330623626709},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7219670414924622},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6575233936309814},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6017158031463623},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5278598666191101},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5163275599479675},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.47749847173690796},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4015599191188812},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20315426588058472},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.19202792644500732},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1257152557373047},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09077325463294983},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2019.8813840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8813840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","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":32,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1529454649","https://openalex.org/W1567242735","https://openalex.org/W1836465849","https://openalex.org/W1932624639","https://openalex.org/W1970504153","https://openalex.org/W2023259872","https://openalex.org/W2034207289","https://openalex.org/W2061319289","https://openalex.org/W2062118960","https://openalex.org/W2088830459","https://openalex.org/W2140881794","https://openalex.org/W2143905117","https://openalex.org/W2148487973","https://openalex.org/W2163605009","https://openalex.org/W2167340365","https://openalex.org/W2218842719","https://openalex.org/W2296228853","https://openalex.org/W2336416123","https://openalex.org/W2563113203","https://openalex.org/W2592770378","https://openalex.org/W2613718673","https://openalex.org/W2901136733","https://openalex.org/W3103648783","https://openalex.org/W4388475847","https://openalex.org/W6620707391","https://openalex.org/W6638667902","https://openalex.org/W6666023844","https://openalex.org/W6681243043","https://openalex.org/W6684191040","https://openalex.org/W6697304529","https://openalex.org/W6756486208"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W3000097931","https://openalex.org/W4281783339","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W2354322770","https://openalex.org/W1964041166"],"abstract_inverted_index":{"Localization":[0],"is":[1,41,64,84,112,130,165],"an":[2,31,45,73],"essential":[3],"technique":[4],"for":[5],"many":[6],"robotic":[7],"tasks":[8],"such":[9],"as":[10],"mapping":[11],"and":[12,80,105,149],"navigation.":[13],"Scan":[14],"matching":[15,36,78,93],"has":[16],"been":[17],"fused":[18],"with":[19,156],"other":[20,157],"sensors":[21],"to":[22,48,66,72,86,113,131],"solve":[23],"the":[24,77,88,110,115,128,133,142,151,162],"problem":[25],"at":[26,38,121,136],"GPS":[27],"restricted":[28],"areas,":[29],"where":[30,109],"accurate":[32],"error":[33,53,97,147],"model":[34,54],"describing":[35],"precision":[37],"various":[39,122],"scenes":[40],"indispensable.":[42],"We":[43,140],"proposed":[44,85,116,163],"end-to-end":[46],"method":[47,117,164],"learn":[49],"a":[50,69,81],"scene":[51],"adaptive":[52],"of":[55,76,90,118,144,161],"LiDAR":[56,70,91],"scan":[57,71,92],"matching.":[58],"A":[59],"CNN":[60],"(Convolutional":[61],"Neural":[62],"Network)":[63],"learnt":[65],"map":[67],"from":[68],"information":[74],"matrix":[75],"result,":[79],"localization":[82,152],"framework":[83],"fuse":[87],"results":[89],"based":[94],"on":[95],"its":[96,119],"model.":[98],"Experiments":[99],"are":[100],"conducted":[101],"using":[102],"both":[103],"simulated":[104],"real":[106,137],"world":[107,138],"data,":[108],"former":[111],"validate":[114],"adaptability":[120],"simple":[123],"but":[124],"typical":[125],"scenes,":[126],"while":[127],"later":[129],"examine":[132,150],"method's":[134],"practicability":[135],"environments.":[139],"demonstrate":[141],"performance":[143],"learning":[145],"covariance":[146],"model,":[148],"accuracy":[153],"by":[154],"comparing":[155],"traditional":[158],"methods.":[159],"Efficiency":[160],"demonstrated.":[166]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}