{"id":"W4233256775","doi":"10.22215/etd/2014-10059","title":"Autonomous Mobile Robot Positioning using Unscented HydridSLAM","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Carnegie Mellon University","keywords":"Simultaneous localization and mapping; Mobile robot; Extended Kalman filter; Unscented transform; Robot; Computer science; Kalman filter; Artificial intelligence; Probabilistic logic; Process (computing); Monte Carlo localization; Field (mathematics); Computer vision; Motion planning; Mathematics; Invariant extended Kalman filter","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":[0.0003665733,0.0003101882,0.0004150397,0.000324297,0.0002429913,0.0006288691,0.0006977454,0.0004822178,0.0009859984],"category_scores_gemma":[0.0009117971,0.0002432182,0.0004129392,0.0004160826,0.000432046,0.0009037404,0.001193232,0.0005348483,0.0004267921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003545945,"about_ca_system_score_gemma":0.0006321992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002312025,"about_ca_topic_score_gemma":0.002114506,"domain_scores_codex":[0.9997337,0.00005512241,0.00001061767,0.00005071991,0.0001306117,0.00001919732],"domain_scores_gemma":[0.9996614,0.000118059,0.00004321239,0.00006385495,0.00009540799,0.00001790025],"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":[0.0002064162,0.00006994351,0.001888914,0.0001653232,0.00008834473,0.0001341064,0.000333039,0.6342248,0.04305507,0.02150207,0.00179839,0.2965336],"study_design_scores_gemma":[0.00001008983,0.00005867622,0.0003460793,0.00000654411,0.000005765582,0.00003542936,0.00002575106,0.9880009,0.006016673,0.002769619,0.002712168,0.00001226087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01832817,0.0001209012,0.9794326,0.00006374121,0.00002040385,0.00001254196,0.0000301236,0.0005747091,0.001416807],"genre_scores_gemma":[0.4837235,0.0002762032,0.509956,0.000115059,0.00003725804,0.0001026853,0.0002257669,0.0001288371,0.005434692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002312025,"threshold_uncertainty_score":0.004597068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006670343292752249,"score_gpt":0.2239667752889031,"score_spread":0.2172964319961509,"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."}}