{"id":"W2785003163","doi":"10.22215/etd/2013-09909","title":"A Comparison of Nonlinear Filters on Mobile Robot Pose Estimation","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Extended Kalman filter; Particle filter; Mobile robot; Kalman filter; Invariant extended Kalman filter; Monte Carlo localization; Robustness (evolution); Robot; Artificial intelligence; Computer science; Control theory (sociology); Noise (video); Simultaneous localization and mapping; Pose; Computer vision; Filter (signal processing)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110983,0.0002596739,0.0004281083,0.0002030323,0.00008559498,0.000138005,0.0008590281,0.0002725881,0.0002702733],"category_scores_gemma":[0.00003779111,0.0002249125,0.0001149887,0.000261903,0.00001886872,0.0002517173,0.00005904077,0.000329885,0.0003272203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002526071,"about_ca_system_score_gemma":0.00005233214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001096934,"about_ca_topic_score_gemma":0.00002220571,"domain_scores_codex":[0.9982233,0.00005207751,0.0005630056,0.0004743991,0.0004641164,0.0002231111],"domain_scores_gemma":[0.9983059,0.0001879098,0.0004156342,0.000827121,0.0001799921,0.00008345809],"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":[0.000103515,0.001251045,0.0002214883,0.0004047872,0.00009945273,0.000007513112,0.004321289,0.23568,0.002455574,0.01130383,0.1598877,0.5842637],"study_design_scores_gemma":[0.0002135091,0.0003569018,0.0007239514,0.000278525,0.00001790857,0.000001433456,0.0002588694,0.981411,0.0138883,0.0002020166,0.002302897,0.0003447106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2707105,0.0007901592,0.6649992,0.0001717398,0.01300509,0.002399151,0.0001082631,0.00136089,0.04645497],"genre_scores_gemma":[0.4770852,0.00005928723,0.5059896,0.0001814424,0.0002525788,0.0001122229,0.004489866,0.00005760991,0.0117722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7457309,"threshold_uncertainty_score":0.9171664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235206374761797,"score_gpt":0.3259508796240459,"score_spread":0.303598815876428,"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."}}