{"id":"W4386902868","doi":"10.1109/tim.2023.3317378","title":"Dynamic SLAM: A Visual SLAM in Outdoor Dynamic Scenes","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Key Research and Development Project of Hainan Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer vision; Computer science; Simultaneous localization and mapping; Orb (optics); Feature (linguistics); Pipeline (software); Robot; Segmentation; Object (grammar); 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":[0.0001800097,0.0001535203,0.0001290844,0.0003723883,0.00009715126,0.00004445613,0.00003996045,0.00006436137,0.00003250758],"category_scores_gemma":[0.000002534089,0.0001668527,0.00004045277,0.000333799,0.00002226839,0.0001136556,5.120671e-7,0.0001162099,0.00004918199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002890266,"about_ca_system_score_gemma":0.00002339239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002962222,"about_ca_topic_score_gemma":0.0006166564,"domain_scores_codex":[0.998964,0.00003207446,0.0002700397,0.0001866057,0.0003405391,0.0002068055],"domain_scores_gemma":[0.9997623,0.00001269234,0.00002279502,0.00009195757,0.00004610402,0.00006413286],"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":[0.00002939235,0.00008450071,0.0001066599,0.00009144489,0.00004421782,0.000003834557,0.0005622723,0.8867382,0.02732643,0.00003201689,0.00002715815,0.0849539],"study_design_scores_gemma":[0.0011681,0.00008827707,0.004170304,0.00008912492,0.00002244345,0.000002511598,0.0004670774,0.9798254,0.01376458,0.00006955438,0.000125911,0.0002067654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4997042,0.00003306819,0.4981872,0.0002157445,0.0009346576,0.0003713328,0.00001370779,0.0003325852,0.0002074936],"genre_scores_gemma":[0.9989473,0.0003243232,0.0004572863,0.00005308538,0.0000059065,0.00006451448,0.00001935112,0.00003020729,0.00009799852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4992431,"threshold_uncertainty_score":0.6804054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02006927565816399,"score_gpt":0.2541174863017155,"score_spread":0.2340482106435515,"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."}}