{"id":"W2468891172","doi":"10.5194/isprs-archives-xli-b1-589-2016","title":"A NEW AUTOMATIC SYSTEM CALIBRATION OF MULTI-CAMERAS AND LIDAR SENSORS","year":2016,"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":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Lidar; Calibration; Point cloud; Computer science; Key (lock); Remote sensing; Computer vision; Mobile mapping; Artificial intelligence; Point (geometry); Real-time computing; Geography; Mathematics","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":[0.0008522221,0.000371463,0.0004263484,0.0007981906,0.0006359515,0.0004231552,0.0008620702,0.00009616314,0.000005715029],"category_scores_gemma":[0.0004461239,0.0002181207,0.0002447501,0.0006917072,0.001718861,0.0004846494,0.0003983952,0.0002330539,0.000001669146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005156177,"about_ca_system_score_gemma":0.0001652827,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4966991,"about_ca_topic_score_gemma":0.06108528,"domain_scores_codex":[0.9963772,0.0002140092,0.001417062,0.0002957688,0.001305974,0.0003900219],"domain_scores_gemma":[0.9972806,0.0008476309,0.00110557,0.0003781024,0.0002250857,0.000163025],"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":[0.00005642408,0.0000096737,0.0002696084,0.00009915989,0.00008630539,2.224373e-7,0.002231182,0.01692675,0.006506214,0.00001970134,0.00003115301,0.9737636],"study_design_scores_gemma":[0.0008672836,0.00009748207,0.002448475,0.0006651752,0.00004682322,0.0000953991,0.001338964,0.9761454,0.01619479,0.001197396,0.0006415611,0.0002612648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0230623,0.00003633372,0.9714031,0.001618339,0.001265307,0.0005364614,0.0000850166,0.00007849375,0.00191463],"genre_scores_gemma":[0.9924509,0.0001787106,0.006959151,0.0002216479,0.00009659483,2.49754e-7,0.00001802649,0.00001478889,0.00005991271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9735023,"threshold_uncertainty_score":0.9560475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407569320864703,"score_gpt":0.2315961651831208,"score_spread":0.2175204719744738,"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."}}