{"id":"https://openalex.org/W4389606792","doi":"https://doi.org/10.1109/ivcnz61134.2023.10344241","title":"Using Deep Learning Depth Maps to Improve Monocular SLAM","display_name":"Using Deep Learning Depth Maps to Improve Monocular SLAM","publication_year":2023,"publication_date":"2023-11-29","ids":{"openalex":"https://openalex.org/W4389606792","doi":"https://doi.org/10.1109/ivcnz61134.2023.10344241"},"language":"en","primary_location":{"id":"doi:10.1109/ivcnz61134.2023.10344241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz61134.2023.10344241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 38th International Conference on Image and Vision Computing New Zealand (IVCNZ)","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/A5093471362","display_name":"Simon Hollows","orcid":null},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Simon Hollows","raw_affiliation_strings":["University of Canterbury,Dept. Mechatronics Engineering,Christchurch,New Zealand","Dept. Mechatronics Engineering, University of Canterbury, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"University of Canterbury,Dept. Mechatronics Engineering,Christchurch,New Zealand","institution_ids":["https://openalex.org/I185492890"]},{"raw_affiliation_string":"Dept. Mechatronics Engineering, University of Canterbury, Christchurch, New Zealand","institution_ids":["https://openalex.org/I185492890"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100730175","display_name":"Richard Green","orcid":"https://orcid.org/0000-0002-8671-8966"},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Richard Green","raw_affiliation_strings":["University of Canterbury,Dept. Computer Science and Software Engineering,Christchurch,New Zealand","Dept. Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"University of Canterbury,Dept. Computer Science and Software Engineering,Christchurch,New Zealand","institution_ids":["https://openalex.org/I185492890"]},{"raw_affiliation_string":"Dept. Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand","institution_ids":["https://openalex.org/I185492890"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5093471362"],"corresponding_institution_ids":["https://openalex.org/I185492890"],"apc_list":null,"apc_paid":null,"fwci":0.4412,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.76806807,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"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/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":0.9998000264167786,"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":0.7841005325317383},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.7126264572143555},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6902049779891968},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6577020287513733},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5918003916740417},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.5818008780479431},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5802986025810242},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5632845759391785},{"id":"https://openalex.org/keywords/simultaneous-localization-and-mapping","display_name":"Simultaneous localization and mapping","score":0.5466161966323853},{"id":"https://openalex.org/keywords/measured-depth","display_name":"Measured depth","score":0.5160769820213318},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49613961577415466},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4913691580295563},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3560921549797058},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2813454568386078},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2374066710472107},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.0880240797996521},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.0815780758857727},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07326745986938477},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.07191944122314453}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7841005325317383},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.7126264572143555},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6902049779891968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6577020287513733},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5918003916740417},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.5818008780479431},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5802986025810242},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5632845759391785},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.5466161966323853},{"id":"https://openalex.org/C113346285","wikidata":"https://www.wikidata.org/wiki/Q6804193","display_name":"Measured depth","level":2,"score":0.5160769820213318},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49613961577415466},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4913691580295563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3560921549797058},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2813454568386078},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2374066710472107},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0880240797996521},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.0815780758857727},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07326745986938477},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.07191944122314453},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivcnz61134.2023.10344241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz61134.2023.10344241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 38th International Conference on Image and Vision Computing New Zealand (IVCNZ)","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":16,"referenced_works":["https://openalex.org/W1612997784","https://openalex.org/W2065906272","https://openalex.org/W2461937780","https://openalex.org/W2606794968","https://openalex.org/W2758772066","https://openalex.org/W2955639361","https://openalex.org/W2963591054","https://openalex.org/W2963906250","https://openalex.org/W2970360421","https://openalex.org/W3013299216","https://openalex.org/W3043971245","https://openalex.org/W3081167590","https://openalex.org/W3096741513","https://openalex.org/W3124420883","https://openalex.org/W4211154833","https://openalex.org/W4285803579"],"related_works":["https://openalex.org/W2556215627","https://openalex.org/W2980723633","https://openalex.org/W2915493008","https://openalex.org/W2003805688","https://openalex.org/W3210711677","https://openalex.org/W4200218943","https://openalex.org/W3111845905","https://openalex.org/W2089613850","https://openalex.org/W3009665706","https://openalex.org/W3010374521"],"abstract_inverted_index":{"This":[0,42,74],"paper":[1],"proposes":[2],"the":[3,27,45,55,68,88,111,131,140,155,167,173],"use":[4],"of":[5,30,49,58,162,175],"a":[6,50,145,160],"neural":[7,94],"network":[8,95],"to":[9,19,25,98,107,128,184],"generate":[10,99],"depth":[11,20,37,80,82,90,101,124,142,157,179],"maps":[12,83,125,143,158,180],"from":[13],"2D":[14],"frames,":[15],"offering":[16],"an":[17],"alternative":[18],"cameras.":[21],"The":[22,92,115],"goal":[23],"is":[24],"address":[26],"accuracy":[28,57],"limitations":[29],"monocular":[31],"SLAM":[32,69],"by":[33],"gaining":[34],"more":[35],"accurate":[36,77],"data":[38,134],"through":[39],"deep":[40],"learning.":[41],"approach":[43,183],"combines":[44],"simplicity":[46],"and":[47,87,172],"cost-effectiveness":[48],"single":[51],"camera":[52],"setup":[53],"with":[54,62,130],"enhanced":[56],"RGB-D":[59,63,117,133],"SLAM.ORB-SLAM":[60],"running":[61],"input":[64],"was":[65,96,119],"used":[66,97,120],"as":[67],"framework":[70],"for":[71,76,121],"this":[72,182],"method.":[73],"allowed":[75],"comparisons":[78],"between":[79,178],"sensor":[81],"in":[84,135,186],"test":[85,113],"datasets":[86],"generated":[89,123,156],"maps.":[91,102],"MiDaS":[93],"estimated":[100],"These":[103],"were":[104,126],"then":[105],"scaled":[106],"create":[108],"consistency":[109,171],"over":[110],"entire":[112],"sequence.":[114],"TUM":[116,132,141],"dataset":[118],"testing.The":[122],"able":[127],"compare":[129],"accuracy.":[136],"On":[137],"one":[138],"dataset,":[139],"had":[144,159],"root":[146],"mean":[147],"square":[148],"error":[149],"(RMSE)":[150],"or":[151],"0.24":[152],"m":[153],"while":[154],"RMSE":[161],"0.18":[163],"m.":[164],"In":[165],"general,":[166],"proposed":[168],"method":[169],"lacks":[170],"lack":[174],"good":[176],"fusion":[177],"causes":[181],"fail":[185],"slow-moving":[187],"scenes.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
