{"id":"W2116983226","doi":"10.5194/isprsarchives-xl-1-219-2014","title":"Feasibility study of using the RoboEarth cloud engine for rapid mapping and tracking with small unmanned aerial systems","year":2014,"lang":"en","type":"article","venue":"The international 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":"","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Quadcopter; Computer science; Global Positioning System; Cloud computing; Point cloud; Real-time computing; Tracking system; Computer vision; Unmanned ground vehicle; Artificial intelligence; Tracking (education); Orientation (vector space); Simultaneous localization and mapping; Robot; Engineering; Kalman filter; Mobile robot","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001640122,0.0003388288,0.0004215356,0.0005617057,0.001004245,0.0005980136,0.0008479707,0.00006648085,0.000001157196],"category_scores_gemma":[0.0004512115,0.0001957967,0.0001818314,0.0006487441,0.00151863,0.0003261932,0.000311239,0.0002702896,1.937666e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003368019,"about_ca_system_score_gemma":0.00009401777,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4147592,"about_ca_topic_score_gemma":0.0747018,"domain_scores_codex":[0.9967762,0.0002814673,0.001215999,0.0002910654,0.001062703,0.0003725959],"domain_scores_gemma":[0.9970945,0.001063733,0.001041865,0.000384397,0.0003184183,0.00009712834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001434603,0.00001971988,0.000311501,0.00008002086,0.00008984968,7.071937e-8,0.00401931,0.2268279,0.001445494,0.000009395816,0.000005050987,0.7670482],"study_design_scores_gemma":[0.001149281,0.0002406684,0.002290901,0.0003230551,0.00006081061,0.00005942057,0.004420205,0.9873732,0.002415945,0.0009218963,0.0005120273,0.0002325477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1293736,0.00002689222,0.8671038,0.0004765273,0.001289633,0.001018513,0.00004513071,0.00003453684,0.0006313803],"genre_scores_gemma":[0.9956386,0.00005461676,0.003946069,0.0001559542,0.0001610798,4.968758e-7,0.00001998068,0.00001352085,0.000009692313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.866265,"threshold_uncertainty_score":0.9421825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224089791190687,"score_gpt":0.2455214626533068,"score_spread":0.2132805647413999,"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."}}