{"id":"W4221112862","doi":"10.3390/drones6040085","title":"Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges","year":2022,"lang":"en","type":"article","venue":"Drones","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Simultaneous localization and mapping; Artificial intelligence; Computer vision; Odometry; Sensor fusion; Computer science; Kalman filter; Robotics; Extended Kalman filter; Search and rescue; Fuse (electrical); Photogrammetry; Object (grammar); Scalability; Robot; Mobile robot; Engineering","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":[0.0001186002,0.00008420871,0.000107343,0.00007098243,0.0001020189,0.0000250814,0.00005402764,0.00003089155,0.000007179662],"category_scores_gemma":[0.00003473811,0.00009175386,0.000003044172,0.00009369889,0.00002450014,0.0001138544,0.00012108,0.00005849398,2.533021e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000213302,"about_ca_system_score_gemma":0.000004229028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001131997,"about_ca_topic_score_gemma":0.00009530163,"domain_scores_codex":[0.9994332,0.00003649576,0.0001322992,0.0002006314,0.00008968262,0.0001076828],"domain_scores_gemma":[0.9997511,0.00006182526,0.00002038245,0.0001255072,0.0000115791,0.00002963619],"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":[0.00003022791,0.00002019937,0.0006701257,0.0002788931,0.000007282064,0.00001613715,0.001431449,0.7825192,0.0006252535,0.0002902726,0.00003166409,0.2140793],"study_design_scores_gemma":[0.00039406,0.00004051824,0.000340419,0.00002838877,0.000005292175,0.000008630155,0.001077724,0.9407093,0.00005862495,0.0002986922,0.05691447,0.0001239171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8409684,0.1318054,0.0244024,0.001278533,0.0006132604,0.0004786795,0.00007118744,0.0001996537,0.0001825212],"genre_scores_gemma":[0.89485,0.1047516,0.0001923477,0.00002410557,0.0000401381,0.000004950044,0.0001165318,0.00001534721,0.000005011022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2139554,"threshold_uncertainty_score":0.3741614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02938577811892392,"score_gpt":0.2340428110544115,"score_spread":0.2046570329354876,"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."}}