{"id":"W4403922951","doi":"10.1145/3671127.3698172","title":"ICON drone: Autonomous indoor exploration using Unmanned Aerial Vehicle for semantic 3D reconstruction","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Drone; Icon; Computer science; Artificial intelligence; Computer vision; Aeronautics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008548506,0.0004484643,0.0002116758,0.0003164278,0.0001646867,0.0003157715,0.0005021309,0.0002782418,0.001553958],"category_scores_gemma":[0.0001338461,0.0001603349,0.0002617887,0.0001670438,0.0002040642,0.0003980929,0.0005882052,0.0002422102,0.0004224616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001723709,"about_ca_system_score_gemma":0.0003024159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005222027,"about_ca_topic_score_gemma":0.008913448,"domain_scores_codex":[0.9999262,0.000008760384,0.00000188216,0.00002061377,0.00003005772,0.00001248586],"domain_scores_gemma":[0.9999576,0.000006152934,0.000004447827,0.00001486707,0.000009233248,0.000007852046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006199123,0.0003059986,0.008731792,0.0003311879,0.0001810333,0.001065281,0.0007079965,0.1972901,0.284195,0.006918766,0.01649914,0.4831538],"study_design_scores_gemma":[0.0001013142,0.0003278771,0.005648756,0.00002866262,0.00003413603,0.00048516,0.0002788767,0.9124551,0.0530541,0.001312327,0.02621992,0.00005374317],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2861196,0.000417173,0.6853321,0.0001422162,0.0001112344,0.0002751593,0.0008724214,0.01393441,0.01279578],"genre_scores_gemma":[0.7020916,0.0001608713,0.2914241,0.00006428183,0.000009127316,0.0001080138,0.00139186,0.000194477,0.004555662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005222027,"threshold_uncertainty_score":0.01038325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349048970872648,"score_gpt":0.2390843087780339,"score_spread":0.2155938190693075,"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."}}