{"id":"W4378365312","doi":"10.1109/mprv.2023.3274770","title":"Exploiting Radio Fingerprints for Simultaneous Localization and Mapping","year":2023,"lang":"en","type":"article","venue":"IEEE Pervasive Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Division of Electrical, Communications and Cyber Systems; National Science Foundation of Sri Lanka; Natural Science Foundation of Sichuan Province; University of Moratuwa; Nanyang Technological University; Agency for Science, Technology and Research; Auburn University; Imperial College London; Singapore University of Technology and Design; University of Alberta; National Science Foundation","keywords":"Simultaneous localization and mapping; Fingerprint (computing); Computer science; Wireless; Artificial intelligence; Computer vision; Fingerprint recognition; Fidelity; Lidar; Real-time computing; Remote sensing; Robot; Telecommunications; Mobile robot; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001668781,0.0001516677,0.0001752615,0.0001959257,0.0002585211,0.00006409904,0.0001135086,0.00009771479,0.000002813917],"category_scores_gemma":[0.0004152454,0.0001711335,0.0000438861,0.0004079871,0.00003878311,0.00007696469,0.00004688872,0.00009817899,0.00002100071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005311112,"about_ca_system_score_gemma":0.000008287139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000363454,"about_ca_topic_score_gemma":0.000002132451,"domain_scores_codex":[0.9990975,0.00001198817,0.0002418077,0.0002165446,0.00009668194,0.0003354933],"domain_scores_gemma":[0.9992279,0.0004793602,0.0000411714,0.0001205533,0.00009266992,0.00003837055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002112508,0.000002963558,0.00116039,0.0002648064,0.00002584818,0.00001392769,0.001917509,0.9294637,0.004339413,0.0002276048,0.0006835936,0.06189815],"study_design_scores_gemma":[0.0002455713,0.00001299531,0.000167232,0.0001176715,0.000006689882,0.000009342666,0.001233505,0.9833743,0.01278874,0.000361852,0.001480263,0.0002018703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3189506,0.0001109217,0.6782094,0.00003826743,0.0004428506,0.0002048902,0.000004922861,0.001919372,0.0001187794],"genre_scores_gemma":[0.996609,0.00008949977,0.002954298,0.00005833044,0.0001757528,0.00001484456,0.00001968734,0.00004782072,0.00003071591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6776585,"threshold_uncertainty_score":0.6978621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278539583512643,"score_gpt":0.2388696402057456,"score_spread":0.2160842443706192,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}