{"id":"W4233405120","doi":"10.1109/iros.2011.6048397","title":"Navigation meshes for realistic multi-layered environments","year":2011,"lang":"en","type":"article","venue":"2011 IEEE/RSJ International Conference on Intelligent Robots and Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Occupancy grid mapping; Computer science; Artificial intelligence; Cluster analysis; Transformation (genetics); Grid; Simultaneous localization and mapping; Robot; Map matching; Grid reference; Process (computing); Matching (statistics); Global Map; Computer vision; Fusion; Polygon mesh; Task (project management); Pattern recognition (psychology); Mobile robot; Global Positioning System; Geography; Mathematics; Engineering; Computer graphics (images)","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":[0.0001911723,0.0003852637,0.0003409313,0.0003318368,0.0004548602,0.0009686392,0.0006584486,0.0008132322,0.00340741],"category_scores_gemma":[0.001643973,0.0003112179,0.0004262172,0.0002738188,0.0004050701,0.001102278,0.001161058,0.0006818667,0.0009289379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004798193,"about_ca_system_score_gemma":0.0005012758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002202142,"about_ca_topic_score_gemma":0.004167782,"domain_scores_codex":[0.9997054,0.00007134146,0.0000125982,0.00006922312,0.0001146991,0.00002679408],"domain_scores_gemma":[0.9996849,0.0001191074,0.00004157644,0.00006987528,0.00006132989,0.00002313062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005469828,0.00003587591,0.001402677,0.0001462091,0.00003908636,0.0003520387,0.000171109,0.856028,0.01647514,0.04841341,0.00281647,0.07406522],"study_design_scores_gemma":[0.000007597305,0.00002156413,0.0004746731,0.0000139632,0.000006100776,0.0001652456,0.00005526074,0.9715014,0.002529511,0.01477675,0.01043233,0.00001554744],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01672733,0.0001079586,0.9785559,0.0001700875,0.00007638054,0.00003274449,0.0001102552,0.0005981075,0.003621349],"genre_scores_gemma":[0.4541977,0.0002720117,0.5375631,0.00009762792,0.00004567959,0.0001186851,0.0003524825,0.0002357773,0.007116852],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.00340741,"threshold_uncertainty_score":0.01139897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1512015041240228,"score_gpt":0.283589264412553,"score_spread":0.1323877602885302,"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."}}