{"id":"W2163003390","doi":"10.1177/0278364908096316","title":"3D Perception and Environment Map Generation for Humanoid Robot Navigation","year":2008,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia","keywords":"Computer vision; Artificial intelligence; Computer science; Humanoid robot; Segmentation; Occupancy grid mapping; Robot; Stereopsis; Noise (video); Mobile robot","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007821175,0.00007788154,0.00009417805,0.0001686822,0.0001905673,0.0000769053,0.0002318823,0.00005300839,0.0000223791],"category_scores_gemma":[0.00004837389,0.00006123339,0.00004716147,0.00004299768,0.00009664207,0.000145812,0.00003323621,0.000234584,0.0000113758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002169271,"about_ca_system_score_gemma":0.00003585251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005544931,"about_ca_topic_score_gemma":0.000002182975,"domain_scores_codex":[0.9986647,0.00006117059,0.0003059881,0.00008357701,0.0007402336,0.0001443633],"domain_scores_gemma":[0.999186,0.0001269915,0.00006541616,0.00009739104,0.0004708787,0.00005333582],"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":[0.00002494711,0.00002446005,0.00009478997,0.00001105489,0.00004845564,0.000007550278,0.0004441805,0.9380569,0.05599423,0.0005303849,0.002152669,0.002610405],"study_design_scores_gemma":[0.000463676,0.0001336402,0.0008014865,0.00003268569,0.00001241163,0.0001341753,0.000106977,0.992466,0.003118193,0.0008295474,0.001819078,0.00008212753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2410865,0.0003633341,0.7547662,0.002619817,0.0007870546,0.0002585254,0.000006331935,0.00001529449,0.00009702377],"genre_scores_gemma":[0.9799013,0.001348778,0.01750247,0.00003604223,0.0009091593,0.000005779693,0.00004003764,0.00002275958,0.0002337178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7388148,"threshold_uncertainty_score":0.2497025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09760256191343164,"score_gpt":0.3254271614327084,"score_spread":0.2278245995192768,"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."}}