{"id":"W2118114969","doi":"10.1109/ccece.2006.277812","title":"Corridor Line Detection for Vision Based Indoor Robot Navigation","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer vision; Computer science; Robustness (evolution); Robot; Vanishing point; Artificial intelligence; Mobile robot; Line (geometry); Intersection (aeronautics); Ground plane; Simulation; Image (mathematics); Engineering; Mathematics","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":[0.0000712976,0.00009576089,0.00008467444,0.00007010101,0.00006117066,0.0000368847,0.00003277656,0.00008298008,0.00002043175],"category_scores_gemma":[0.00001026588,0.00009275605,0.00004485915,0.0001407019,0.000007632798,0.00007998085,0.000002588747,0.00004648002,0.0000153813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005526203,"about_ca_system_score_gemma":0.000007479012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004801577,"about_ca_topic_score_gemma":0.00005286873,"domain_scores_codex":[0.9994712,0.000008157479,0.0001879772,0.0001123165,0.00009145393,0.0001288554],"domain_scores_gemma":[0.999737,0.0000410817,0.00002271495,0.00009885477,0.00007260288,0.00002778025],"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":[0.00001211058,0.00001766106,0.00005524333,0.00003669578,0.000002279504,3.253733e-7,0.000002644356,0.9301633,0.06309727,0.000130125,0.0005092041,0.005973123],"study_design_scores_gemma":[0.000432663,0.00006800672,0.0005394794,0.00001474189,0.000008456574,6.241232e-7,0.000002910048,0.8112823,0.1861959,0.0001664727,0.00118955,0.00009882544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05621914,0.00001792512,0.9423427,0.00004829065,0.0003320001,0.0002538507,0.000005371212,0.0003134014,0.0004673337],"genre_scores_gemma":[0.9885141,0.000001300769,0.01074441,0.00004424284,0.000179524,0.00002119157,0.0002671508,0.00003083629,0.000197234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.932295,"threshold_uncertainty_score":0.3782482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006358937090533056,"score_gpt":0.2112562746636289,"score_spread":0.2048973375730958,"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."}}