{"id":"https://openalex.org/W7133500781","doi":"https://doi.org/10.48550/arxiv.2603.02936","title":"Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency","display_name":"Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133500781","doi":"https://doi.org/10.48550/arxiv.2603.02936"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.02936","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02936","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.02936","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093930758","display_name":"Nicholas Carlotti","orcid":"https://orcid.org/0009-0006-1475-3400"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carlotti, Nicholas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052452333","display_name":"Michele Antonazzi","orcid":"https://orcid.org/0000-0001-6396-7567"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonazzi, Michele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044918876","display_name":"Elia Cereda","orcid":"https://orcid.org/0000-0001-8888-3538"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cereda, Elia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058862171","display_name":"Mirko Nava","orcid":"https://orcid.org/0000-0002-3736-0419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nava, Mirko","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090235510","display_name":"Nicola Basilico","orcid":"https://orcid.org/0000-0002-4512-3480"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Basilico, Nicola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061890705","display_name":"Daniele Palossi","orcid":"https://orcid.org/0000-0003-4487-0836"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Palossi, Daniele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052119291","display_name":"Alessandro Giusti","orcid":"https://orcid.org/0000-0003-1240-0768"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Giusti, Alessandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.3050000071525574,"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":0.3050000071525574,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.15860000252723694,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.05739999935030937,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/pose","display_name":"Pose","score":0.6962000131607056},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.6223000288009644},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5698000192642212},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5321999788284302},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5238000154495239},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.5188000202178955},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.486299991607666},{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.4668999910354614}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7696999907493591},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.6962000131607056},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.689300000667572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6575000286102295},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.6223000288009644},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5698000192642212},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5321999788284302},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.5188000202178955},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.4668999910354614},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C23903533","wikidata":"https://www.wikidata.org/wiki/Q17122739","display_name":"Reprojection error","level":3,"score":0.36959999799728394},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.3693000078201294},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.02936","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02936","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.02936","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02936","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,55],"consider":[1],"the":[2,7,31,36,39,44,52,70,89,120,190],"task":[3],"of":[4,10,17,78,150,172,183],"visually":[5],"estimating":[6],"relative":[8],"pose":[9,114],"a":[11,18,21,79,85,101,134,159],"drone":[12,71],"racing":[13],"gate":[14],"in":[15,43,76,84,139,152,156],"front":[16,77],"nano-quadrotor,":[19],"using":[20,63],"convolutional":[22],"neural":[23],"network":[24],"pre-trained":[25,40],"on":[26],"simulated":[27],"data":[28,175],"to":[29,35,51],"regress":[30],"gate's":[32],"pose.":[33],"Due":[34],"sim-to-real":[37],"gap,":[38],"model":[41],"underperforms":[42],"real":[45,65],"world":[46],"and":[47,145,154,176],"must":[48],"be":[49],"adapted":[50],"target":[53],"domain.":[54],"propose":[56],"an":[57,73,148,180],"unsupervised":[58],"domain":[59],"adaptation":[60],"(UDA)":[61],"approach":[62,127],"only":[64],"image":[66],"sequences":[67,81],"collected":[68],"by":[69,94],"flying":[72],"arbitrary":[74],"trajectory":[75],"gate;":[80],"are":[82,117],"annotated":[83],"self-supervised":[86],"fashion":[87],"with":[88,119,166,179],"drone's":[90,121],"odometry":[91],"as":[92,167,169],"measured":[93],"its":[95],"onboard":[96],"sensors.":[97],"On":[98],"this":[99],"dataset,":[100],"state":[102],"consistency":[103],"loss":[104],"enforces":[105],"that":[106,116,125],"two":[107],"images":[108],"acquired":[109],"at":[110],"different":[111],"times":[112],"yield":[113],"predictions":[115],"consistent":[118],"odometry.":[122],"Results":[123],"indicate":[124],"our":[126],"outperforms":[128],"other":[129],"SoA":[130],"UDA":[131],"approaches,":[132],"has":[133],"low":[135],"mean":[136],"absolute":[137],"error":[138],"position":[140,153],"(x=26,":[141],"y=28,":[142],"z=10":[143],"cm)":[144],"orientation":[146,157],"($\u03c8$=13${^{\\circ}}$),":[147],"improvement":[149],"40%":[151],"37%":[155],"over":[158],"baseline.":[160],"The":[161],"approach's":[162],"effectiveness":[163],"is":[164],"appreciable":[165],"few":[168],"10":[170],"minutes":[171],"real-world":[173],"flight":[174],"yields":[177],"models":[178],"inference":[181],"time":[182],"30.4ms":[184],"(33":[185],"fps)":[186],"when":[187],"deployed":[188],"aboard":[189],"Crazyflie":[191],"2.1":[192],"Brushless":[193],"nano-drone.":[194]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-05T00:00:00"}