{"id":"https://openalex.org/W2963376049","doi":"https://doi.org/10.1109/icassp.2019.8683446","title":"Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles","display_name":"Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2963376049","doi":"https://doi.org/10.1109/icassp.2019.8683446","mag":"2963376049"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8683446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5086326894","display_name":"Chengze Wang","orcid":"https://orcid.org/0000-0003-2349-6427"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengze Wang","raw_affiliation_strings":["School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi&#x2019;an, 710072, P. R. China","School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi&#x2019;an, 710072, P. R. China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100334801","display_name":"Yuan Yuan","orcid":"https://orcid.org/0000-0003-3352-0662"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Yuan","raw_affiliation_strings":["School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi&#x2019;an, 710072, P. R. China","School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi&#x2019;an, 710072, P. R. China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100341186","display_name":"Qi Wang","orcid":"https://orcid.org/0000-0001-6164-6786"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Wang","raw_affiliation_strings":["School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi&#x2019;an, 710072, P. R. China","School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi&#x2019;an, 710072, P. R. China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.2745,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57974251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2252","last_page":"2256"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998000264167786,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9954000115394592,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8207210302352905},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.7783454060554504},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7063308954238892},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6730136871337891},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6315162181854248},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.6281659603118896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6191020607948303},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5536231398582458},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4921531081199646},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.4384228587150574},{"id":"https://openalex.org/keywords/inertia","display_name":"Inertia","score":0.4243013262748718},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.3105735182762146},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.20529061555862427},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17009013891220093}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8207210302352905},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.7783454060554504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7063308954238892},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6730136871337891},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6315162181854248},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.6281659603118896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6191020607948303},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5536231398582458},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4921531081199646},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.4384228587150574},{"id":"https://openalex.org/C110407247","wikidata":"https://www.wikidata.org/wiki/Q122508","display_name":"Inertia","level":2,"score":0.4243013262748718},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3105735182762146},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.20529061555862427},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17009013891220093},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8683446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W292075967","https://openalex.org/W603908379","https://openalex.org/W1527702126","https://openalex.org/W1803059841","https://openalex.org/W2071499765","https://openalex.org/W2108134361","https://openalex.org/W2115579991","https://openalex.org/W2133665775","https://openalex.org/W2150066425","https://openalex.org/W2171740948","https://openalex.org/W2259424905","https://openalex.org/W2333777777","https://openalex.org/W2340897893","https://openalex.org/W2474281075","https://openalex.org/W2520707372","https://openalex.org/W2535547924","https://openalex.org/W2561074213","https://openalex.org/W2608018946","https://openalex.org/W2609883120","https://openalex.org/W2951234442","https://openalex.org/W2963906250","https://openalex.org/W2964314455","https://openalex.org/W3100388886","https://openalex.org/W3102327032","https://openalex.org/W6610537083","https://openalex.org/W6618372016","https://openalex.org/W6685261749","https://openalex.org/W6703848168","https://openalex.org/W6736260464","https://openalex.org/W6744864977"],"related_works":["https://openalex.org/W2979950214","https://openalex.org/W87609089","https://openalex.org/W3024737167","https://openalex.org/W2414561716","https://openalex.org/W3161199934","https://openalex.org/W2303855011","https://openalex.org/W2312326526","https://openalex.org/W2412578866","https://openalex.org/W3105866016","https://openalex.org/W4312703710"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,70,125],"present":[4],"iDVO":[5],"(inertia-embedded":[6],"deep":[7,30,174],"visual":[8,15],"odometry),":[9],"a":[10,44],"self-supervised":[11],"learning":[12],"based":[13],"monocular":[14,173],"odometry":[16],"(VO)":[17],"for":[18],"road":[19,57,110],"vehicles.":[20],"When":[21],"modelling":[22],"the":[23,34,38,50,54,68,72,78,84,88,96,107,113,117,127,133,141,150,160,164],"geometric":[24],"consistency":[25],"within":[26],"adjacent":[27],"frames,":[28],"most":[29,66],"VO":[31,175],"methods":[32],"ignore":[33],"temporal":[35,114],"continuity":[36],"of":[37,67,109],"camera":[39,92,137],"pose,":[40],"which":[41,82,145],"results":[42,165],"in":[43,49,65,135,149],"very":[45],"severe":[46],"jagged":[47],"fluctuation":[48],"velocity":[51],"curves.":[52],"With":[53],"observation":[55],"that":[56],"vehicles":[58,111],"tend":[59],"to":[60,76,86,131,171],"perform":[61],"smooth":[62],"dynamic":[63,128],"characteristics":[64],"time,":[69],"design":[71],"inertia":[73],"loss":[74],"function":[75],"describe":[77],"abnormal":[79],"motion":[80,138],"variation,":[81],"assists":[83],"model":[85],"learn":[87],"consecutiveness":[89,115],"from":[90],"long-term":[91],"ego-motion.":[93],"Based":[94],"on":[95,159],"recurrent":[97],"convolutional":[98],"neural":[99],"network":[100],"(RCNN)":[101],"architecture,":[102],"our":[103],"method":[104,156],"implicitly":[105],"models":[106],"dynamics":[108],"and":[112,144,163,176],"by":[116,139],"extended":[118],"Long":[119],"Short-Term":[120],"Memory":[121],"(LSTM)":[122],"block.":[123],"Furthermore,":[124],"develop":[126],"hard-edge":[129],"mask":[130],"handle":[132],"non-consistency":[134,152],"fast":[136],"blocking":[140],"boundary":[142],"part":[143],"generates":[146],"more":[147],"efficiency":[148],"whole":[151],"mask.":[153],"The":[154],"proposed":[155],"is":[157],"evaluated":[158],"KITTI":[161],"dataset,":[162],"demonstrate":[166],"state-of-the-art":[167],"performance":[168],"with":[169],"respect":[170],"other":[172],"SLAM":[177],"approaches.":[178]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
