{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002621546,0.0004969836,0.0004524644,0.0005037068,0.0002268248,0.0004029236,0.0007100696,0.0004045495,0.0009971162],"category_scores_gemma":[0.0006537202,0.0002529089,0.0004531242,0.0005524102,0.0001563525,0.0005995133,0.0006749881,0.0004320275,0.0004698636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002644437,"about_ca_system_score_gemma":0.0006037275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005643487,"about_ca_topic_score_gemma":0.005520742,"domain_scores_codex":[0.9996564,0.00004947888,0.00001272314,0.0000727056,0.0001586181,0.00005001383],"domain_scores_gemma":[0.9997589,0.00003075284,0.00003125521,0.0000627093,0.0001037271,0.00001275395],"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":[0.0003358874,0.0001066309,0.004114898,0.00009613828,0.0001183889,0.0002155463,0.0001746348,0.2853761,0.09889448,0.002818856,0.002490483,0.605258],"study_design_scores_gemma":[0.000009220583,0.00006518017,0.001176542,0.000003600859,0.000009676698,0.000110837,0.00003934503,0.9819104,0.01472582,0.0003249713,0.001612703,0.00001175527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06844521,0.0001474636,0.9279082,0.00004665841,0.00004023814,0.00003372378,0.00006001838,0.001576913,0.001741503],"genre_scores_gemma":[0.7179306,0.0001238284,0.2796688,0.0000379044,0.00001407427,0.00003731304,0.0002810383,0.0001067475,0.001799634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005643487,"threshold_uncertainty_score":0.01122129,"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."}}