{"id":"https://openalex.org/W3003233851","doi":"https://doi.org/10.1109/iros40897.2019.8968467","title":"DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints","display_name":"DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3003233851","doi":"https://doi.org/10.1109/iros40897.2019.8968467","mag":"3003233851"},"language":"en","primary_location":{"id":"doi:10.1109/iros40897.2019.8968467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros40897.2019.8968467","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5103164501","display_name":"Liming Han","orcid":"https://orcid.org/0000-0001-6379-2752"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liming Han","raw_affiliation_strings":["CloudMinds Technologies Inc,AI Department,Beijing,China,100102","AI Department, CloudMinds Technologies Inc, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CloudMinds Technologies Inc,AI Department,Beijing,China,100102","institution_ids":[]},{"raw_affiliation_string":"AI Department, CloudMinds Technologies Inc, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103234012","display_name":"Yimin Lin","orcid":"https://orcid.org/0000-0001-8096-4819"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yimin Lin","raw_affiliation_strings":["CloudMinds Technologies Inc,AI Department,Beijing,China,100102","AI Department, CloudMinds Technologies Inc, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CloudMinds Technologies Inc,AI Department,Beijing,China,100102","institution_ids":[]},{"raw_affiliation_string":"AI Department, CloudMinds Technologies Inc, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055770524","display_name":"Guoguang Du","orcid":"https://orcid.org/0000-0001-7534-2396"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guoguang Du","raw_affiliation_strings":["CloudMinds Technologies Inc,AI Department,Beijing,China,100102","AI Department, CloudMinds Technologies Inc, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CloudMinds Technologies Inc,AI Department,Beijing,China,100102","institution_ids":[]},{"raw_affiliation_string":"AI Department, CloudMinds Technologies Inc, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066958531","display_name":"Shiguo Lian","orcid":"https://orcid.org/0000-0003-4308-7049"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiguo Lian","raw_affiliation_strings":["CloudMinds Technologies Inc,AI Department,Beijing,China,100102","AI Department, CloudMinds Technologies Inc, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CloudMinds Technologies Inc,AI Department,Beijing,China,100102","institution_ids":[]},{"raw_affiliation_string":"AI Department, CloudMinds Technologies Inc, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103164501"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":75.3619,"has_fulltext":false,"cited_by_count":116,"citation_normalized_percentile":{"value":0.99829031,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6906","last_page":"6913"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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":0.9998999834060669,"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/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/T10638","display_name":"Optical measurement and interference techniques","score":0.9979000091552734,"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":0.8261157274246216},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.7756766676902771},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7396798133850098},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.7132198214530945},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7088355422019958},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.6530149579048157},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5857415795326233},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.5275040864944458},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.49363958835601807},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.45969879627227783},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43531420826911926},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.41686180233955383},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.14604106545448303},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.14246833324432373}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8261157274246216},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.7756766676902771},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7396798133850098},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.7132198214530945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7088355422019958},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.6530149579048157},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5857415795326233},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.5275040864944458},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.49363958835601807},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.45969879627227783},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43531420826911926},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.41686180233955383},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.14604106545448303},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.14246833324432373},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","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},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros40897.2019.8968467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros40897.2019.8968467","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1521470386","https://openalex.org/W1808303360","https://openalex.org/W2104974755","https://openalex.org/W2118223742","https://openalex.org/W2166132830","https://openalex.org/W2216550548","https://openalex.org/W2300779272","https://openalex.org/W2344109021","https://openalex.org/W2461937780","https://openalex.org/W2474281075","https://openalex.org/W2482726005","https://openalex.org/W2520707372","https://openalex.org/W2535547924","https://openalex.org/W2538522345","https://openalex.org/W2559203616","https://openalex.org/W2560474170","https://openalex.org/W2565829216","https://openalex.org/W2598706937","https://openalex.org/W2609883120","https://openalex.org/W2745859992","https://openalex.org/W2765767940","https://openalex.org/W2793043514","https://openalex.org/W2890949887","https://openalex.org/W2891299851","https://openalex.org/W2952348863","https://openalex.org/W2962816904","https://openalex.org/W2962867206","https://openalex.org/W2962891637","https://openalex.org/W2962987986","https://openalex.org/W2963412495","https://openalex.org/W2963583471","https://openalex.org/W2963619659","https://openalex.org/W2964314455","https://openalex.org/W3102327032","https://openalex.org/W3106440972","https://openalex.org/W3124420883","https://openalex.org/W3125449081","https://openalex.org/W4230766380","https://openalex.org/W4246614213","https://openalex.org/W6638529194","https://openalex.org/W6697658144","https://openalex.org/W6721610202","https://openalex.org/W6754824001","https://openalex.org/W6755939869"],"related_works":["https://openalex.org/W2979950214","https://openalex.org/W87609089","https://openalex.org/W2414561716","https://openalex.org/W3024737167","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":{"This":[0],"paper":[1],"presents":[2],"an":[3,124],"self-supervised":[4],"deep":[5],"learning":[6,155],"network":[7,90,106,110],"for":[8],"monocular":[9],"visual":[10],"inertial":[11],"odometry":[12],"(named":[13],"DeepVIO).":[14],"DeepVIO":[15,85,152,170],"provides":[16],"absolute":[17],"trajectory":[18],"estimation":[19,133],"by":[20,48,93,113],"directly":[21],"merging":[22],"2D":[23,87],"optical":[24,60,72,88,100],"flow":[25,61,73,89],"feature":[26],"(OFF)":[27],"and":[28,40,52,62,76,81,102,107,139,147,162,179],"Inertial":[29],"Measurement":[30],"Unit":[31],"(IMU)":[32],"data.":[33,181],"Specifically,":[34],"it":[35],"firstly":[36],"estimates":[37],"the":[38,94,108,115,136,167,172],"depth":[39],"dense":[41],"3D":[42,55,59,71,99],"point":[43],"cloud":[44],"of":[45,96,160,174],"each":[46],"scene":[47],"using":[49],"stereo":[50],"sequences,":[51],"then":[53],"obtains":[54],"geometric":[56],"constraints":[57],"including":[58],"6-DoF":[63],"pose":[64,132],"as":[65],"supervisory":[66],"signals.":[67],"Note":[68],"that":[69,151],"such":[70],"shows":[74],"robustness":[75],"accuracy":[77,161],"to":[78,129,166],"dynamic":[79],"objects":[80],"textureless":[82],"environments.":[83],"In":[84],"training,":[86],"is":[91],"constrained":[92],"projection":[95],"its":[97],"corresponding":[98],"flow,":[101],"LSTM-style":[103],"IMU":[104,125,131],"preintegration":[105],"fusion":[109],"are":[111],"learned":[112],"minimizing":[114],"loss":[116],"functions":[117],"from":[118],"ego-motion":[119],"constraints.":[120],"Furthermore,":[121],"we":[122],"employ":[123],"status":[126],"update":[127],"scheme":[128],"improve":[130],"through":[134],"updating":[135],"additional":[137],"gyroscope":[138],"accelerometer":[140],"bias.":[141],"The":[142],"experimental":[143],"results":[144],"on":[145],"KITTI":[146],"EuRoC":[148],"datasets":[149],"show":[150],"outperforms":[153],"state-of-the-art":[154],"based":[156],"methods":[157],"in":[158],"terms":[159],"data":[163],"adaptability.":[164],"Compared":[165],"traditional":[168],"methods,":[169],"reduces":[171],"impacts":[173],"inaccurate":[175],"Camera-IMU":[176],"calibrations,":[177],"unsynchronized":[178],"missing":[180]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}
