{"id":"W6929453562","doi":"10.48550/arxiv.2508.06053","title":"ReNiL: Event-Driven Pedestrian Bayesian Localization Using IMU for Real-World Applications","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Robustness (evolution); Bayesian probability; Bayesian inference; Inference; Probabilistic logic; Estimator; Particle filter; Pedestrian","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":[0.0008224996,0.001115136,0.0008678114,0.0008962909,0.000308359,0.0006987778,0.002254101,0.0008768849,0.002733916],"category_scores_gemma":[0.002873324,0.0005664161,0.0005969762,0.0008990163,0.0004666826,0.001551868,0.002162076,0.001134113,0.001761004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007975709,"about_ca_system_score_gemma":0.001027126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01310769,"about_ca_topic_score_gemma":0.02377466,"domain_scores_codex":[0.9995054,0.0001057662,0.00001746517,0.000165287,0.0001361468,0.0000697795],"domain_scores_gemma":[0.999504,0.0001192645,0.00006363043,0.0001209841,0.0001428289,0.00004929769],"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":[0.000570695,0.0002798273,0.007516228,0.0002778191,0.000196551,0.0002460523,0.0002132885,0.5277345,0.007706326,0.01232513,0.03451979,0.4084139],"study_design_scores_gemma":[0.00001457221,0.00002627717,0.0005042403,0.00001135504,0.00000887244,0.00003152061,0.000013143,0.9918587,0.001564326,0.00350866,0.002447731,0.00001061625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01403353,0.0004241939,0.9732385,0.0001999236,0.00008388126,0.00004829844,0.0009171389,0.009269794,0.001784733],"genre_scores_gemma":[0.5854658,0.0005290957,0.3989711,0.0003839096,0.000135709,0.0002191172,0.006519605,0.0008382295,0.00693739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01310769,"threshold_uncertainty_score":0.02606279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05372568298798028,"score_gpt":0.2220143992598297,"score_spread":0.1682887162718494,"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."}}