{"id":"https://openalex.org/W2498889283","doi":"https://doi.org/10.1109/i2mtc.2016.7520579","title":"Egomotion estimation for monocular camera visual odometer","display_name":"Egomotion estimation for monocular camera visual odometer","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2498889283","doi":"https://doi.org/10.1109/i2mtc.2016.7520579","mag":"2498889283"},"language":"en","primary_location":{"id":"doi:10.1109/i2mtc.2016.7520579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc.2016.7520579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings","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/A5063560115","display_name":"Maurizio Bevilacqua","orcid":"https://orcid.org/0000-0001-9487-0258"},"institutions":[{"id":"https://openalex.org/I82284825","display_name":"Cranfield University","ror":"https://ror.org/05cncd958","country_code":"GB","type":"education","lineage":["https://openalex.org/I82284825"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"M. Bevilacqua","raw_affiliation_strings":["Cranfield University, Bedfordshire, MK, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cranfield University, Bedfordshire, MK, United Kingdom","institution_ids":["https://openalex.org/I82284825"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085012064","display_name":"Antonios Tsourdos","orcid":"https://orcid.org/0000-0002-3966-7633"},"institutions":[{"id":"https://openalex.org/I82284825","display_name":"Cranfield University","ror":"https://ror.org/05cncd958","country_code":"GB","type":"education","lineage":["https://openalex.org/I82284825"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"A. Tsourdos","raw_affiliation_strings":["Cranfield University, Bedfordshire, MK, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cranfield University, Bedfordshire, MK, United Kingdom","institution_ids":["https://openalex.org/I82284825"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002449661","display_name":"Andrew Starr","orcid":"https://orcid.org/0000-0001-9046-560X"},"institutions":[{"id":"https://openalex.org/I82284825","display_name":"Cranfield University","ror":"https://ror.org/05cncd958","country_code":"GB","type":"education","lineage":["https://openalex.org/I82284825"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"A. Starr","raw_affiliation_strings":["Cranfield University, Bedfordshire, MK, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cranfield University, Bedfordshire, MK, United Kingdom","institution_ids":["https://openalex.org/I82284825"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82284825"],"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":"1","last_page":"6"},"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":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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"}}],"keywords":[{"id":"https://openalex.org/keywords/odometer","display_name":"Odometer","score":0.917798638343811},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.8471372127532959},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8054545521736145},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7106608748435974},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.6473335027694702},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.5488789081573486},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5461188554763794},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5054824352264404},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.49816274642944336},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.47771596908569336},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4627167582511902},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4167451858520508},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.11795735359191895},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09425324201583862}],"concepts":[{"id":"https://openalex.org/C93717769","wikidata":"https://www.wikidata.org/wiki/Q745105","display_name":"Odometer","level":2,"score":0.917798638343811},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.8471372127532959},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8054545521736145},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106608748435974},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.6473335027694702},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.5488789081573486},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5461188554763794},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5054824352264404},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.49816274642944336},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.47771596908569336},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4627167582511902},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4167451858520508},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.11795735359191895},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09425324201583862},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/i2mtc.2016.7520579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc.2016.7520579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4399999976158142}],"awards":[{"id":"https://openalex.org/G2325447594","display_name":null,"funder_award_id":"EP/I033246/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G6491275806","display_name":null,"funder_award_id":"EP/J011630/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W110099284","https://openalex.org/W1531122638","https://openalex.org/W1639227073","https://openalex.org/W1664553916","https://openalex.org/W1858012409","https://openalex.org/W1991832245","https://openalex.org/W2015996585","https://openalex.org/W2069635875","https://openalex.org/W2110729215","https://openalex.org/W2111308925","https://openalex.org/W2113560487","https://openalex.org/W2115601136","https://openalex.org/W2118877769","https://openalex.org/W2126464006","https://openalex.org/W2130103520","https://openalex.org/W2164406796","https://openalex.org/W2170942349","https://openalex.org/W2296147781","https://openalex.org/W3040777582","https://openalex.org/W4243425824","https://openalex.org/W6637056485","https://openalex.org/W6648404801","https://openalex.org/W6677548441","https://openalex.org/W6679388247","https://openalex.org/W6780413204"],"related_works":["https://openalex.org/W3006563365","https://openalex.org/W4386394365","https://openalex.org/W2954249689","https://openalex.org/W2626414811","https://openalex.org/W321362102","https://openalex.org/W4313484789","https://openalex.org/W2001736731","https://openalex.org/W2913604885","https://openalex.org/W4302306787","https://openalex.org/W4300939778"],"abstract_inverted_index":{"Visual":[0],"odometer":[1,169],"is":[2,58,82,98,123,157,186,193,238],"a":[3,12,18,22,53,62,84,87,124,142,166,183,199,245],"technique":[4],"that":[5],"permit":[6],"to":[7,26,47,60,100,112,117,171,222,240,257],"measure":[8],"the":[9,15,49,76,91,102,139,152,163,173,196,204,207,210,235,242],"velocity":[10],"of":[11,17,71,75,126,141,165,175,198,201,209,213],"vehicle":[13],"by":[14],"elaboration":[16],"video":[19],"source":[20],"from":[21],"camera":[23,144,185],"solidly":[24],"fixed":[25],"it.":[27,259],"Actual":[28],"cameras":[29],"are":[30,251,255],"cheap":[31],"and":[32,36,93,115,154,206,231,253],"have":[33,219],"high":[34,55],"resolution":[35,63],"fast":[37],"frame":[38],"rate,":[39],"with":[40,52,86,179,182,244],"these":[41],"characteristics":[42],"it":[43],"become":[44,146],"really":[45,54],"easy":[46],"estimate":[48,223],"ego":[50],"motion":[51,181,212],"accuracy.":[56],"It":[57],"possible":[59],"achieve":[61],"in":[64,79,159,188],"position":[65],"around":[66],"20-30":[67],"cm.":[68],"The":[69,96,249],"use":[70,140],"VO":[72],"as":[73,107],"part":[74],"navigation":[77],"system":[78],"autonomous":[80],"vehicles":[81],"nowadays":[83],"field":[85],"big":[88],"interest":[89,202],"for":[90,119],"science":[92],"industry":[94],"community.":[95],"aim":[97],"not":[99],"substitute":[101],"current":[103],"navigational":[104],"systems":[105],"such":[106],"GPS":[108],"or":[109],"INS,":[110],"but":[111,137],"provide":[113],"robustness":[114],"safety":[116],"operations,":[118],"example":[120],"when":[121],"there":[122],"loss":[125],"signal":[127],"coverage.":[128],"Extended":[129],"work":[130,156],"has":[131,145],"been":[132,220],"done":[133],"using":[134],"stereo":[135],"camera,":[136],"recently":[138],"monocular":[143,184],"more":[147],"popular.":[148],"A":[149],"research":[150],"about":[151],"concept":[153],"related":[155],"covered":[158],"this":[160,189,214,224],"work.":[161],"Also":[162],"implementation":[164],"simple":[167],"visual":[168],"(VO)":[170],"recover":[172],"speed":[174],"an":[176],"on-road":[177],"car":[178],"straight":[180],"presented":[187],"document.":[190],"This":[191],"algorithm":[192,243],"based":[194],"on":[195],"detection":[197],"region":[200],"within":[203],"road":[205],"computation":[208],"relative":[211,225],"region.":[215],"Three":[216],"different":[217],"methods":[218],"tested":[221],"motion:":[226],"optical":[227],"flow,":[228],"feature":[229,232],"matching":[230],"tracking.":[233],"Finally":[234],"best":[236],"method":[237],"used":[239],"test":[241],"real":[246],"traffic":[247],"sequence.":[248],"results":[250],"compared":[252],"modifications":[254],"proposed":[256],"improve":[258]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
