{"id":"W2991137242","doi":"10.5194/isprs-archives-xlii-2-w17-413-2019","title":"MAPPING QUALITY EVALUATION OF MONOCULAR SLAM SOLUTIONS FOR MICRO AERIAL VEHICLES","year":2019,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Simultaneous localization and mapping; Computer vision; Artificial intelligence; Point cloud; Computer science; Ground truth; Quadcopter; Monocular; Laser scanning; Lidar; Trajectory; Robotics; Mobile mapping; Benchmark (surveying); Remote sensing; Mobile robot; Geography; Robot; Engineering; Cartography; Laser","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":[0.001266467,0.0008308332,0.0006278669,0.001214342,0.0004222969,0.0008176811,0.001035387,0.0005620179,0.001766752],"category_scores_gemma":[0.003864465,0.0001697309,0.0005026097,0.001247616,0.0003514341,0.0007837849,0.001146923,0.0003721222,0.0004465498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006766588,"about_ca_system_score_gemma":0.0008480647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01039165,"about_ca_topic_score_gemma":0.01139913,"domain_scores_codex":[0.9980019,0.0003221902,0.0001334158,0.0003507603,0.001014574,0.0001771953],"domain_scores_gemma":[0.9983801,0.0004034941,0.0001416233,0.0003687911,0.0006162756,0.00008978536],"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":[0.00181012,0.0003653943,0.01506362,0.00142963,0.0004925894,0.0002266529,0.0002412673,0.3624668,0.02455608,0.002210754,0.02163939,0.5694978],"study_design_scores_gemma":[0.0001518643,0.0008521965,0.02815711,0.0001189395,0.00007922854,0.0002686887,0.0004654839,0.9319589,0.02652451,0.00146355,0.009900088,0.00005932664],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8502946,0.003482054,0.1174558,0.0003115816,0.0005681714,0.0002878558,0.007379103,0.007775476,0.01244543],"genre_scores_gemma":[0.9372609,0.0003646312,0.05007489,0.00007838863,0.00002288467,0.00007808011,0.01086659,0.000169411,0.001084218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01039165,"threshold_uncertainty_score":0.02066231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03359626429663748,"score_gpt":0.2740031788766673,"score_spread":0.2404069145800298,"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."}}