{"id":"https://openalex.org/W7167828346","doi":"https://doi.org/10.48550/arxiv.2607.07306","title":"Wrench-Based Bayesian Pose Estimation via Matrix--Fisher Gaussian Inference","display_name":"Wrench-Based Bayesian Pose Estimation via Matrix--Fisher Gaussian Inference","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7167828346","doi":"https://doi.org/10.48550/arxiv.2607.07306"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2607.07306","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.07306","pdf_url":"https://arxiv.org/pdf/2607.07306","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2607.07306","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140343020","display_name":"Jianyu Chen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jianyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123198425","display_name":"L Yang","orcid":"https://orcid.org/0000-0002-2032-9327","observed_orcids":["https://orcid.org/0000-0002-2032-9327"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135945610","display_name":"Yibang Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yibang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079258091","display_name":"Domenico Campolo","orcid":"https://orcid.org/0000-0001-6930-0413","observed_orcids":["https://orcid.org/0000-0001-6930-0413"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Campolo, Domenico","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5036426252","display_name":"Cyrus Mostajeran","orcid":"https://orcid.org/0000-0001-8910-9755","observed_orcids":["https://orcid.org/0000-0001-8910-9755"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mostajeran, Cyrus","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":null,"has_fulltext":true,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.3984000086784363,"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"}},"topics":[{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.3984000086784363,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.329800009727478,"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/T10571","display_name":"Robotic Mechanisms and Dynamics","score":0.0877000018954277,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.7720000147819519},{"id":"https://openalex.org/keywords/force-and-torque-measurement","display_name":"force and torque measurement","score":0.8399999737739563},{"id":"https://openalex.org/keywords/quasistatic-contact","display_name":"quasistatic contact","score":0.9340000152587891},{"id":"https://openalex.org/keywords/schur-complement","display_name":"Schur complement","score":0.9750000238418579},{"id":"https://openalex.org/keywords/object-pose-estimation","display_name":"object pose estimation","score":0.5040000081062317}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6840999722480774},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5483999848365784},{"id":"https://openalex.org/C11210021","wikidata":"https://www.wikidata.org/wiki/Q1520713","display_name":"Linearization","level":3,"score":0.5315999984741211},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5185999870300293},{"id":"https://openalex.org/C200331156","wikidata":"https://www.wikidata.org/wiki/Q506041","display_name":"Jacobian matrix and determinant","level":2,"score":0.49889999628067017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4666999876499176},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4408999979496002},{"id":"https://openalex.org/C29302406","wikidata":"https://www.wikidata.org/wiki/Q154411","display_name":"Wrench","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4011000096797943},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.38370001316070557},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3815000057220459},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3452000021934509},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3440000116825104},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C108598597","wikidata":"https://www.wikidata.org/wiki/Q124255","display_name":"Conic section","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.26100000739097595}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2607.07306","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.07306","pdf_url":"https://arxiv.org/pdf/2607.07306","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2607.07306","doi":"https://doi.org/10.48550/arxiv.2607.07306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07306","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2607.07306","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.07306","pdf_url":"https://arxiv.org/pdf/2607.07306","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7167828346.pdf","grobid_xml":"https://content.openalex.org/works/W7167828346.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7082666296","https://openalex.org/W4378191432","https://openalex.org/W2013818565","https://openalex.org/W2117765697","https://openalex.org/W2113830700","https://openalex.org/W7159800608","https://openalex.org/W2205211475","https://openalex.org/W3046199742","https://openalex.org/W2268615161","https://openalex.org/W3009955308"],"abstract_inverted_index":{"In":[0,158],"this":[1],"paper,":[2],"a":[3,25,59,79,92,102,120,185],"residual-safeguarded":[4,137],"local":[5,60,98,109,177],"Matrix":[6],"Fisher--Gaussian":[7],"(MFG)":[8],"inference":[9],"method":[10],"is":[11,50,63,84,140],"developed":[12],"for":[13],"wrench-based":[14],"pose":[15,39,171],"estimation":[16],"on":[17,35],"$\\mathrm{SO}(3)\\times\\mathbb{R}^3$.":[18],"The":[19,115],"force/torque":[20],"measurements":[21],"are":[22],"modeled":[23],"by":[24,65],"quasi-static":[26,191],"contact":[27],"system":[28],"in":[29],"which":[30,125],"the":[31,36,46,55,67,73,87,97,107,127,145,153,159,164],"predicted":[32],"wrench":[33,69,156],"depends":[34],"unknown":[37],"object":[38],"through":[40,149],"an":[41],"implicit":[42],"equilibrium":[43],"state.":[44],"Since":[45],"resulting":[47,165],"nonlinear":[48],"likelihood":[49],"not":[51],"globally":[52],"conjugate":[53],"to":[54,78,143,174],"coupled":[56,80],"MFG":[57,81,104],"family,":[58],"Bayesian":[61],"update":[62,144],"constructed":[64],"linearizing":[66],"reduced":[68,88],"residual":[70,89,168],"and":[71,95,111,170,176,180],"matching":[72,106],"induced":[74],"Gauss--Newton":[75],"posterior":[76,100,113],"model":[77,118],"distribution.":[82],"It":[83],"shown":[85],"that":[86,96,151],"Jacobian":[90],"has":[91],"Schur-complement":[93],"form,":[94],"quadratic":[99],"admits":[101],"closed-form":[103],"approximation":[105],"prescribed":[108],"first-":[110],"second-order":[112],"coefficients.":[114],"same":[116],"sensitivity":[117],"yields":[119],"compensated":[121],"rotational":[122],"information":[123],"score,":[124],"characterizes":[126],"weakest":[128],"locally":[129],"informative":[130],"attitude":[131],"direction":[132],"after":[133],"translational":[134],"compensation.":[135],"A":[136],"recentering":[138],"algorithm":[139],"further":[141],"introduced":[142],"linearization":[146],"point":[147],"only":[148],"candidates":[150],"decrease":[152],"recomputed":[154],"whitened":[155],"residual.":[157],"tested":[160],"sparse":[161],"prior-mismatch":[162],"regimes,":[163],"estimator":[166],"reduces":[167],"merit":[169],"error":[172],"relative":[173],"single-pass":[175],"baseline":[178],"variants,":[179],"controlled":[181],"robot":[182],"experiments":[183],"provide":[184],"proof":[186],"of":[187],"concept":[188],"under":[189],"calibrated":[190],"conditions.":[192]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-07-10T00:00:00"}