{"id":"W2500606337","doi":"10.1007/978-981-10-0934-1_29","title":"Indoor Map Aiding/Map Matching Smartphone Navigation Using Auxiliary Particle Filter","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Map matching; Particle filter; Computer science; Computer vision; Inertial navigation system; Artificial intelligence; Matching (statistics); Dead reckoning; Position (finance); Pedestrian; Filter (signal processing); Engineering; Global Positioning System; Inertial frame of reference; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001811086,0.0008595031,0.0008335182,0.0006188034,0.0003146956,0.0005639424,0.0007049815,0.000716361,0.003118082],"category_scores_gemma":[0.0005448371,0.0003543864,0.0006403051,0.0009055065,0.0001456892,0.0005967945,0.0008012635,0.0005720622,0.002632604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001900118,"about_ca_system_score_gemma":0.0005516288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005542871,"about_ca_topic_score_gemma":0.006288022,"domain_scores_codex":[0.9997453,0.00002276699,0.000007943583,0.00007507572,0.0001108138,0.00003798783],"domain_scores_gemma":[0.9998382,0.0000220598,0.000008966478,0.0000432985,0.00007778901,0.000009606159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000358542,0.0001302413,0.002433918,0.0001810004,0.00008558943,0.0002360826,0.0001375199,0.03052998,0.0731283,0.00236523,0.0101198,0.8802938],"study_design_scores_gemma":[0.00004918171,0.0001939114,0.00589758,0.0000359889,0.0001171941,0.0007225084,0.00008778171,0.9132967,0.0587549,0.002083816,0.01870949,0.00005103793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02197584,0.0002888986,0.9677039,0.00005804946,0.0002644501,0.00004287464,0.0001949567,0.00352098,0.005950029],"genre_scores_gemma":[0.4864493,0.000463085,0.4973789,0.0001266311,0.0001199113,0.00009047688,0.0008668667,0.0002837984,0.01422105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005542871,"threshold_uncertainty_score":0.0110212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009111098190118554,"score_gpt":0.204409363759139,"score_spread":0.1952982655690204,"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."}}