{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003575839,0.0006802522,0.00057128,0.001119227,0.0004191953,0.00104996,0.0007992336,0.0007325326,0.00142524],"category_scores_gemma":[0.001441109,0.0003188429,0.0003394939,0.00131379,0.0003852953,0.001595768,0.001845495,0.0005825126,0.001395091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002212079,"about_ca_system_score_gemma":0.0003787016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001370192,"about_ca_topic_score_gemma":0.00149139,"domain_scores_codex":[0.9993442,0.0001208146,0.00002211139,0.0001328972,0.0002627615,0.0001171174],"domain_scores_gemma":[0.9994122,0.0001135768,0.0000809262,0.0002394558,0.0001221457,0.00003180193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003631144,0.0001081464,0.003489666,0.0002242829,0.00007274246,0.0005111905,0.0002790384,0.04672829,0.1151046,0.01248272,0.005007642,0.8156285],"study_design_scores_gemma":[0.00009793577,0.0004542029,0.004262758,0.00007849603,0.0001350518,0.002820895,0.000310203,0.7743503,0.1470347,0.0233981,0.04686636,0.0001909952],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02798721,0.0004877059,0.963464,0.0001236284,0.0001077278,0.00003266997,0.0001020822,0.003495882,0.004199064],"genre_scores_gemma":[0.7782413,0.0005701572,0.2177128,0.0001688226,0.0001232972,0.0000746796,0.0002476318,0.0001464799,0.002714832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00142524,"threshold_uncertainty_score":0.004767895,"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."}}