{"id":"https://openalex.org/W7125891081","doi":null,"title":"Vision-Based Relative Pose Estimation of Space Objects for Proximity Operations Using Lab-Validated Deep Learning","display_name":"Vision-Based Relative Pose Estimation of Space Objects for Proximity Operations Using Lab-Validated Deep Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7125891081"},"language":"en","primary_location":{"id":"pmh:oai:arpi.unipi.it:11568/1345007","doi":null,"is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1345007","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5059082503","display_name":"Matteo Forasassi","orcid":"https://orcid.org/0009-0004-1081-2683","observed_orcids":["https://orcid.org/0009-0004-1081-2683"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matteo FORASASSI","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079651096","display_name":"Giordana Bucchioni","orcid":"https://orcid.org/0000-0002-5654-0730","observed_orcids":["https://orcid.org/0000-0002-5654-0730"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Giordana BUCCHIONI","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027904339","display_name":"Lorenzo Pollini","orcid":"https://orcid.org/0000-0002-9165-2594","observed_orcids":["https://orcid.org/0000-0002-9165-2594"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lorenzo POLLINI","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61613403,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T11701","display_name":"Space Satellite Systems and Control","score":0.9984999895095825,"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/T11701","display_name":"Space Satellite Systems and Control","score":0.9984999895095825,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.00039999998989515007,"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/T11082","display_name":"Spacecraft Dynamics and Control","score":0.0003000000142492354,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/relative-pose-estimation","display_name":"relative pose estimation","score":0.9559999704360962},{"id":"https://openalex.org/keywords/monocular-vision","display_name":"monocular vision","score":0.6980000138282776},{"id":"https://openalex.org/keywords/6d-pose-estimation","display_name":"6D pose estimation","score":0.8989999890327454},{"id":"https://openalex.org/keywords/synthetic-data-augmentation","display_name":"synthetic data augmentation","score":0.7200000286102295},{"id":"https://openalex.org/keywords/domain-shift","display_name":"domain shift","score":0.9610000252723694},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"extended Kalman filter","score":0.9509999752044678},{"id":"https://openalex.org/keywords/rigid-body-dynamics","display_name":"rigid body dynamics","score":0.7900000214576721},{"id":"https://openalex.org/keywords/on-orbit-servicing","display_name":"on-orbit servicing","score":0.7089999914169312},{"id":"https://openalex.org/keywords/space-debris-removal","display_name":"space debris removal","score":0.9679999947547913},{"id":"https://openalex.org/keywords/laboratory-validation","display_name":"laboratory validation","score":0.41600000858306885},{"id":"https://openalex.org/keywords/real-time-pose-estimation","display_name":"real-time pose estimation","score":0.6439999938011169}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.8151000142097473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7312999963760376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.728600025177002},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6722000241279602},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6680999994277954},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5498999953269958},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5382999777793884},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.4575999975204468},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C187107819","wikidata":"https://www.wikidata.org/wiki/Q835696","display_name":"NASA Deep Space Network","level":3,"score":0.3944000005722046},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.3659999966621399},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.3571999967098236},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C158829959","wikidata":"https://www.wikidata.org/wiki/Q1640606","display_name":"Monocular vision","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.3294999897480011},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.3183000087738037},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3125},{"id":"https://openalex.org/C2780945871","wikidata":"https://www.wikidata.org/wiki/Q194274","display_name":"Backup","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C153715457","wikidata":"https://www.wikidata.org/wiki/Q254183","display_name":"Augmented reality","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.2624000012874603}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:arpi.unipi.it:11568/1345007","doi":null,"is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1345007","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7154386188","https://openalex.org/W3108463056","https://openalex.org/W7161046898","https://openalex.org/W3177333872","https://openalex.org/W3123179458","https://openalex.org/W4380047483","https://openalex.org/W2810225457","https://openalex.org/W4281488473","https://openalex.org/W2960917303","https://openalex.org/W4407338319"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,44,69],"scalable":[4],"framework":[5],"for":[6,47,112],"real-time,":[7],"6-DOF":[8],"pose":[9,49],"estimation":[10],"of":[11,76,81],"uncooperative":[12],"space":[13],"objects":[14],"using":[15],"monocular":[16],"cameras.":[17],"We":[18],"address":[19],"dataset":[20],"scarcity":[21],"by":[22],"generating":[23],"mission-specific":[24],"synthetic":[25,63],"data":[26],"augmented":[27],"with":[28,68],"style":[29],"randomization":[30],"to":[31],"mitigate":[32],"domain":[33,95],"shift.":[34],"A":[35],"lightweight":[36],"YOLOv11n-pose":[37],"model":[38],"extracts":[39],"keypoints,":[40],"which":[41],"feed":[42],"into":[43],"RANSAC-PnP":[45],"solver":[46],"initial":[48],"estimation,":[50],"refined":[51],"through":[52],"an":[53],"Extended":[54],"Kalman":[55],"Filter":[56],"leveraging":[57],"rigid":[58],"body":[59],"dynamics.":[60],"Validation":[61],"against":[62],"sequences":[64],"and":[65,78,94,102,115],"laboratory":[66],"experiments":[67],"1:1":[70],"replica":[71],"demonstrates":[72],"mean":[73],"orientation":[74],"errors":[75,80],"3.9\u00b0":[77],"translation":[79],"1.1%,":[82],"matching":[83],"state-of-the-art":[84],"performance.":[85],"The":[86],"pipeline":[87],"maintains":[88],"robustness":[89],"under":[90],"challenging":[91],"lighting":[92],"conditions":[93],"shift,":[96],"achieving":[97],"~50":[98],"fps":[99,104],"on":[100,105,109],"GPU":[101],"~5":[103],"CPU,":[106],"enabling":[107],"deployment":[108],"resource-constrained":[110],"platforms":[111],"on-orbit":[113],"servicing":[114],"debris":[116],"removal":[117],"missions.":[118]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-01-28T00:00:00"}
