{"id":"W3048761334","doi":"10.3390/s21093091","title":"DOE-SLAM: Dynamic Object Enhanced Visual SLAM","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Robustness (evolution); Simultaneous localization and mapping; Computer science; Object (grammar); Pose; Monocular; Exploit; Trajectory; Video tracking; Robot; Mobile robot","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":[0.00004569845,0.0001542149,0.0001699984,0.00006296311,0.00005380741,0.00004325267,0.00005530324,0.00009929438,0.0001282913],"category_scores_gemma":[0.00004654556,0.0001649289,0.00007308993,0.0002140435,0.00002010833,0.00004190606,0.00001487962,0.0001295263,0.0001744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006980185,"about_ca_system_score_gemma":0.0000242916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009284101,"about_ca_topic_score_gemma":0.00008500385,"domain_scores_codex":[0.9991397,0.00003321782,0.0001968753,0.0002019229,0.0001617061,0.0002665979],"domain_scores_gemma":[0.9995901,0.00004017624,0.00001950095,0.0002066824,0.00007712182,0.0000664344],"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":[0.000004330114,0.00002093427,0.00003009482,0.00004708857,0.00003698918,0.00005480244,0.0002703623,0.8874276,0.108064,0.0003265323,0.0001897991,0.003527484],"study_design_scores_gemma":[0.0002825403,0.00002334443,0.0006179383,0.00002924965,0.00001877303,0.00001127225,0.0001577347,0.8369957,0.1599455,0.0001207402,0.001537085,0.0002600636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9578585,0.000165946,0.0309759,0.0000779019,0.0007493842,0.00008983908,0.000006678361,0.0003680888,0.009707741],"genre_scores_gemma":[0.996823,0.0001312792,0.001047594,0.00006038454,0.00008353388,0.000002824232,0.00006611313,0.00005237516,0.001732869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0518815,"threshold_uncertainty_score":0.6725606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004179687517758217,"score_gpt":0.2191375038064673,"score_spread":0.2149578162887091,"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."}}