{"id":"W2892992993","doi":"10.5194/isprs-annals-iv-1-171-2018","title":"ENHANCEMENT OF REAL-TIME SCAN MATCHING FOR UAV INDOOR NAVIGATION USING VEHICLE MODEL","year":2018,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"GNSS applications; Computer science; Inertial measurement unit; Lidar; Simultaneous localization and mapping; Initialization; Flight test; Artificial intelligence; Ranging; Inertial navigation system; Iterative closest point; Computer vision; Real-time computing; Quadcopter; Point cloud; Global Positioning System; Simulation; Remote sensing; Mobile robot; Engineering; Inertial frame of reference; Geography; Robot","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005749845,0.0001036991,0.0001633868,0.0001407167,0.0002480178,0.00008000728,0.0001002901,0.00005406191,0.000001622601],"category_scores_gemma":[0.00005614421,0.00008249903,0.00006102468,0.0003622331,0.0002228236,0.0002240994,0.00002863317,0.00004314261,6.374505e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001554659,"about_ca_system_score_gemma":0.00003798075,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02465849,"about_ca_topic_score_gemma":0.0005642923,"domain_scores_codex":[0.998983,0.00002286898,0.0004341226,0.00008759102,0.0002871516,0.0001853075],"domain_scores_gemma":[0.9992356,0.00004500826,0.000236755,0.0001320443,0.0003089384,0.00004164948],"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":[0.00002163322,0.000004943695,0.00002574974,0.00009331245,0.00001444493,1.881834e-8,0.001268253,0.4436174,0.1326499,0.0000159897,0.00005066291,0.4222378],"study_design_scores_gemma":[0.0001035131,0.0000612865,0.00004838683,0.0001172479,0.000008304015,0.000001004958,0.0001341044,0.7636545,0.2351008,0.0006756608,0.00002173072,0.00007345963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4528302,0.000005910563,0.5466504,0.00004969628,0.0001031132,0.0001396234,0.000009111827,0.00001506417,0.0001968482],"genre_scores_gemma":[0.9864736,0.00002806246,0.01337402,0.00007132836,0.00003381226,1.200182e-7,0.000007749343,0.000006702192,0.000004671202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5336433,"threshold_uncertainty_score":0.9818364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04247233160270562,"score_gpt":0.3001719084318552,"score_spread":0.2576995768291496,"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."}}