{"id":"W2150708363","doi":"10.1109/rose.2011.6058524","title":"Robust pseudo-random fiducial marker for indoor localization","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Fiducial marker; Computer vision; Computer science; Artificial intelligence; Robustness (evolution); Coding (social sciences); Redundancy (engineering); Decoding methods; Mathematics; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004860503,0.0006374746,0.0005476364,0.000805411,0.0002738284,0.0005047912,0.0009223726,0.0006989431,0.001388718],"category_scores_gemma":[0.002486375,0.0002823206,0.0003160766,0.0007263923,0.000493506,0.0009926325,0.0008638412,0.0005643344,0.001276338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002924181,"about_ca_system_score_gemma":0.0003364262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004118987,"about_ca_topic_score_gemma":0.0005154791,"domain_scores_codex":[0.9989141,0.0003750723,0.00003585594,0.0001404695,0.0004803389,0.00005409662],"domain_scores_gemma":[0.9990901,0.0002300258,0.0001808094,0.0002630424,0.0002050539,0.00003097889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005173045,0.00005195236,0.00158724,0.0003610145,0.00008204116,0.0003640598,0.000252534,0.1481967,0.1800197,0.04725781,0.00651775,0.6147918],"study_design_scores_gemma":[0.00004942905,0.0004106503,0.001496285,0.00005804088,0.0000538898,0.0008573221,0.00004959479,0.7966776,0.1577231,0.009231314,0.03325476,0.000138085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00772536,0.0004018885,0.9898165,0.00005767538,0.0000635838,0.00001479772,0.00003550445,0.001121768,0.0007628959],"genre_scores_gemma":[0.376436,0.0004987693,0.6199231,0.00008548983,0.00009957641,0.000103081,0.0002365209,0.0002036487,0.002413811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001388718,"threshold_uncertainty_score":0.004645765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03541470106754002,"score_gpt":0.1996216683915614,"score_spread":0.1642069673240214,"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."}}