{"id":"https://openalex.org/W4402352528","doi":"https://doi.org/10.1109/ijcnn60899.2024.10649955","title":"MD2VO: Enhancing Monocular Visual Odometry through Minimum Depth Difference","display_name":"MD2VO: Enhancing Monocular Visual Odometry through Minimum Depth Difference","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352528","doi":"https://doi.org/10.1109/ijcnn60899.2024.10649955"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10649955","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10649955","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5081639449","display_name":"Pengzhi Li","orcid":"https://orcid.org/0009-0005-4763-4320"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengzhi Li","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104670622","display_name":"Chengshuai Tang","orcid":"https://orcid.org/0009-0002-3018-5790"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengshuai Tang","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111319846","display_name":"Yifu Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifu Duan","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100765840","display_name":"Zhiheng Li","orcid":"https://orcid.org/0000-0002-5972-4826"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiheng Li","raw_affiliation_strings":["Tsinghua University,Tsinghua Shenzhen International Graduate School,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Shenzhen International Graduate School,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9994000196456909,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9968000054359436,"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/visual-odometry","display_name":"Visual odometry","score":0.8742232322692871},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.8624095916748047},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6584650874137878},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6557782888412476},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5214490294456482},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.2705032229423523}],"concepts":[{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.8742232322692871},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.8624095916748047},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6584650874137878},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6557782888412476},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5214490294456482},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2705032229423523}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10649955","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10649955","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W612478963","https://openalex.org/W1479736493","https://openalex.org/W1532362218","https://openalex.org/W1612997784","https://openalex.org/W1970504153","https://openalex.org/W1991544872","https://openalex.org/W2028936041","https://openalex.org/W2064451896","https://openalex.org/W2115579991","https://openalex.org/W2124313187","https://openalex.org/W2150066425","https://openalex.org/W2159456898","https://openalex.org/W2474281075","https://openalex.org/W2520707372","https://openalex.org/W2535547924","https://openalex.org/W2598706937","https://openalex.org/W2951234442","https://openalex.org/W2955639361","https://openalex.org/W2962816904","https://openalex.org/W2963412495","https://openalex.org/W2968288611","https://openalex.org/W3035563424","https://openalex.org/W3048510980","https://openalex.org/W3081167590","https://openalex.org/W3091687063","https://openalex.org/W3102327032","https://openalex.org/W3106440972","https://openalex.org/W3182318349","https://openalex.org/W4214520160","https://openalex.org/W4224313034","https://openalex.org/W4313484789","https://openalex.org/W4322616103","https://openalex.org/W4379806231","https://openalex.org/W4385245566","https://openalex.org/W4388994505","https://openalex.org/W6685261749","https://openalex.org/W6863975828"],"related_works":["https://openalex.org/W4386394365","https://openalex.org/W3006563365","https://openalex.org/W2593793146","https://openalex.org/W3035056458","https://openalex.org/W2954249689","https://openalex.org/W2626414811","https://openalex.org/W2767907963","https://openalex.org/W4313484789","https://openalex.org/W2001736731","https://openalex.org/W2964314455"],"abstract_inverted_index":{"Monocular":[0],"visual":[1,8,39],"odometry":[2,40],"is":[3],"an":[4],"indispensable":[5],"technique":[6],"in":[7,84,106,120],"signal":[9],"tasks,":[10],"including":[11],"robotics":[12],"and":[13,19,47,90,111],"navigation.":[14],"However,":[15],"the":[16,121,131],"scale":[17,87],"ambiguity":[18],"poor":[20],"generalizability":[21],"of":[22,108,133],"monocular":[23,43],"systems":[24],"remain":[25],"significant":[26],"challenges.":[27],"To":[28],"address":[29],"this":[30],"issue,":[31],"we":[32,114],"propose":[33],"a":[34,53,69],"novel":[35],"framework":[36],"to":[37,124],"enhance":[38],"(VO)":[41],"using":[42,93],"depth":[44,54,60,65,71,78,127],"estimation":[45,66],"algorithms":[46],"geometry-based":[48,135],"methods.":[49,137],"Our":[50],"approach":[51,75],"employs":[52],"point":[55],"filtering":[56],"method":[57,100],"that":[58,98,116],"utilizes":[59],"information":[61,79],"output":[62],"from":[63,80],"multiple":[64],"models":[67,119],"with":[68],"minimum":[70],"difference":[72],"strategy.":[73],"This":[74],"effectively":[76],"combines":[77],"different":[81],"models,":[82],"resulting":[83],"more":[85],"accurate":[86],"factor":[88],"recovery":[89],"trajectory":[91],"prediction":[92],"PnP.":[94],"Experiment":[95],"results":[96],"demonstrate":[97],"our":[99],"outperforms":[101],"current":[102],"learning-based":[103],"VO":[104,122,136],"methods":[105],"terms":[107],"generalization":[109],"capability":[110],"accuracy.":[112],"Additionally,":[113],"show":[115],"incorporating":[117],"pre-trained":[118],"system":[123],"obtain":[125],"pseudo-labeled":[126],"data":[128],"significantly":[129],"enhances":[130],"performance":[132],"existing":[134]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}