{"id":"W3141654846","doi":"10.1109/imtc.2006.328617","title":"Application of Segmented 2D Probabilistic Occupancy Maps for Mobile Robot Sensing and Navigation","year":2006,"lang":"en","type":"article","venue":"Conference proceedings - IEEE Instrumentation/Measurement Technology Conference","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Occupancy grid mapping; Occupancy; Mobile robot; Probabilistic logic; Artificial intelligence; Computer science; Computer vision; Segmentation; Mobile robot navigation; Robot; Representation (politics); Sensor fusion; Robot control; Engineering","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":[0.0003535312,0.000446102,0.0003980461,0.0007629789,0.0003078546,0.0007449708,0.0005748108,0.0003861598,0.0009227581],"category_scores_gemma":[0.002311105,0.0003726144,0.0003860802,0.0007929588,0.0004537065,0.0007824908,0.0007609504,0.0002925899,0.0002074712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004357392,"about_ca_system_score_gemma":0.000434823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002549775,"about_ca_topic_score_gemma":0.002760609,"domain_scores_codex":[0.9995925,0.000110605,0.00001340136,0.00006658593,0.0001775093,0.00003950078],"domain_scores_gemma":[0.9993505,0.0003356001,0.00006036249,0.00009242885,0.0001401023,0.00002097559],"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":[0.0003894764,0.00006884356,0.003362966,0.0002946886,0.0001042682,0.0004772916,0.0006332188,0.5137367,0.06305555,0.02787625,0.00234794,0.3876528],"study_design_scores_gemma":[0.000009376014,0.00006239173,0.001191234,0.00001112102,0.00001353023,0.0002357632,0.00007308363,0.9746786,0.01172316,0.008117728,0.003854444,0.00002951232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01901922,0.0002380604,0.9787635,0.00007398867,0.0000417091,0.00003068104,0.00006976013,0.0005166293,0.001246431],"genre_scores_gemma":[0.6204076,0.0003833337,0.3777427,0.00007919763,0.00006040042,0.0001056632,0.0002064795,0.00008549181,0.0009290662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002549775,"threshold_uncertainty_score":0.005069852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01992147595894431,"score_gpt":0.2382604256194673,"score_spread":0.218338949660523,"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."}}