{"id":"W4221152040","doi":"10.1109/lra.2022.3174971","title":"Intensity Image-Based LiDAR Fiducial Marker System","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Fiducial marker; Lidar; Point cloud; Coordinate system; Computer vision; Artificial intelligence; Intensity (physics); Computer science; Remote sensing; Physics; Optics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001591973,0.0001381441,0.0001595419,0.0001246283,0.0002417985,0.00008449645,0.00007628456,0.00003515244,0.00002375089],"category_scores_gemma":[0.000007130519,0.0001557336,0.00004870345,0.0001704365,0.00002928616,0.00007981266,0.0000183605,0.0001467054,0.00001126555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001546202,"about_ca_system_score_gemma":0.00001320603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001003119,"about_ca_topic_score_gemma":0.000001060235,"domain_scores_codex":[0.9991493,0.00004922544,0.0002336112,0.0001601991,0.000224648,0.0001829695],"domain_scores_gemma":[0.9996657,0.00003226765,0.00004895947,0.0001549504,0.00004057844,0.00005751316],"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":[0.000006825545,0.00001056553,0.0001847167,0.0001002215,0.00001659481,0.00001603881,0.00006465026,0.9812254,0.01354396,0.0003266261,0.004255625,0.0002487174],"study_design_scores_gemma":[0.000301743,0.00001852837,0.001710973,0.00001955015,0.00002316557,0.00001236176,0.00006445591,0.9960981,0.001032199,0.000011083,0.0005221631,0.000185665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2860225,0.00003073991,0.7089957,0.001984098,0.00176702,0.0002287236,0.00002422964,0.0006590746,0.0002879414],"genre_scores_gemma":[0.9926898,0.00000230833,0.006236424,0.0008502649,0.0001099333,0.000013121,0.00005210926,0.00003404986,0.00001198788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7066673,"threshold_uncertainty_score":0.6350632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006510953459521566,"score_gpt":0.1823857374736349,"score_spread":0.1758747840141133,"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."}}