{"id":"W3205358776","doi":"10.1109/icra48506.2021.9560763","title":"MCMC Occupancy Grid Mapping with a Data-Driven Patch Prior","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Occupancy; Occupancy grid mapping; Markov chain Monte Carlo; Computer science; Grid; Sampling (signal processing); Gibbs sampling; Posterior probability; Markov chain; Monte Carlo method; Bayesian probability; Algorithm; Artificial intelligence; Mobile robot; Statistics; Mathematics; Computer vision; Machine learning; Robot; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001262557,0.0006484449,0.001532245,0.0008967693,0.000738778,0.001337414,0.003343007,0.001522075,0.004711819],"category_scores_gemma":[0.00700781,0.001030794,0.001138909,0.00170085,0.001201563,0.001502061,0.001549251,0.002507396,0.0009806051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105835,"about_ca_system_score_gemma":0.001748507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02714557,"about_ca_topic_score_gemma":0.03498206,"domain_scores_codex":[0.9990945,0.0002968757,0.00003577728,0.0002544313,0.000212034,0.0001064564],"domain_scores_gemma":[0.9970547,0.001932209,0.0001331156,0.00039692,0.000379112,0.0001039008],"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":[0.0001153508,0.00003843254,0.001799606,0.00007191142,0.00005816864,0.00009339289,0.00009794241,0.9363745,0.0007796544,0.02029822,0.003873862,0.03639893],"study_design_scores_gemma":[0.000009519244,0.000004670374,0.0001134077,0.000004366286,0.000003824728,0.00001128421,0.000006115344,0.9926152,0.000138123,0.00647802,0.0006097847,0.000005667583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01100695,0.0002367747,0.9857522,0.0002173374,0.0000703179,0.00007407887,0.0004188199,0.0008737377,0.00134982],"genre_scores_gemma":[0.4229908,0.0003143359,0.5681447,0.0004252862,0.000188023,0.0005470188,0.002469091,0.0004667927,0.004453906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02714557,"threshold_uncertainty_score":0.05397511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888228099391639,"score_gpt":0.2207511496032468,"score_spread":0.1918688686093304,"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."}}