{"id":"W2969608721","doi":"10.3390/s19173699","title":"SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"RANSAC; Computer science; Artificial intelligence; Feature (linguistics); Simultaneous localization and mapping; Computer vision; Extended Kalman filter; Tracking (education); Kalman filter; Object (grammar); Video tracking; Algorithm; Robot; Mobile robot; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001804796,0.000196938,0.0002384671,0.0001750242,0.00006597917,0.00004401324,0.00007964703,0.0001284399,0.00001539209],"category_scores_gemma":[0.00002195249,0.0002246436,0.00007866973,0.0001804501,0.00001594009,0.000112075,0.00001091469,0.0002256118,0.00001094481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002215109,"about_ca_system_score_gemma":0.000006912303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001890083,"about_ca_topic_score_gemma":0.000005020242,"domain_scores_codex":[0.9988688,0.00004443679,0.0002746135,0.0002748709,0.000157002,0.0003803425],"domain_scores_gemma":[0.9996774,0.0000605878,0.00004421491,0.0001608546,0.00001163335,0.00004527272],"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":[0.000004760145,0.00001679985,0.0006390164,0.0000821197,0.00002034279,0.000002903454,0.0005384912,0.9766872,0.009501386,0.00001333139,3.148552e-7,0.01249332],"study_design_scores_gemma":[0.0005462096,0.0000262885,0.0004571949,0.00004286927,0.00001553827,0.000005690505,0.0006184432,0.9966534,0.001201515,0.00002298649,0.0001436049,0.0002662962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4003401,0.00009023176,0.5988005,0.000002618561,0.0001191592,0.0002948609,0.00000240715,0.00007648877,0.0002736692],"genre_scores_gemma":[0.9139813,0.00003303241,0.0856257,0.00001109961,0.00003665567,0.000008104714,0.00004731652,0.00009298477,0.0001637624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5136412,"threshold_uncertainty_score":0.9160701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0076174099912578,"score_gpt":0.2060402319151467,"score_spread":0.1984228219238889,"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."}}