{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001345675,0.0003629321,0.0004024538,0.0003150525,0.0001336595,0.00006762559,0.0003026024,0.000348813,0.000003999294],"category_scores_gemma":[0.0001070571,0.0004005404,0.00004443172,0.0005386416,0.0004747103,0.000333218,0.00005220938,0.0002744949,0.000003315344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002936268,"about_ca_system_score_gemma":0.0000344884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001842986,"about_ca_topic_score_gemma":0.00001786658,"domain_scores_codex":[0.9979818,0.00000504129,0.0006863265,0.0005411954,0.0003603525,0.0004252239],"domain_scores_gemma":[0.9981816,0.00003166754,0.0003170671,0.0002201452,0.00120082,0.00004863453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002081212,0.00004354205,0.00385481,0.0004791933,0.00003298142,2.830228e-7,0.00008321193,0.0003937362,0.9239777,0.009468347,0.00005191191,0.06159342],"study_design_scores_gemma":[0.0008388622,0.0001510447,0.0004989579,0.0002851882,0.00006515189,0.00001363571,0.001039834,0.05602694,0.8805156,0.06003052,0.0001326419,0.0004016063],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7393044,0.0001167782,0.2573766,0.0001171763,0.0000936376,0.001546363,0.00003072208,0.001179632,0.0002346066],"genre_scores_gemma":[0.9821033,0.00005376531,0.01703945,0.000007206688,0.00002612863,0.0006663872,0.00005663456,0.00003833719,0.000008816367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2427988,"threshold_uncertainty_score":0.9998447,"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."}}