{"id":"W2922326277","doi":"10.1109/tits.2019.2900548","title":"A Probability Occupancy Grid Based Approach for Real-Time LiDAR Ground Segmentation","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Occupancy grid mapping; Lidar; Segmentation; Robustness (evolution); Computer science; Grid; Computer vision; Occupancy; Artificial intelligence; Remote sensing; Engineering; Geography","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":[0.0005397432,0.0007819642,0.001108855,0.001273864,0.0005939513,0.001022549,0.002056391,0.0007431787,0.002475352],"category_scores_gemma":[0.001663426,0.0006191172,0.0008765123,0.001981288,0.0005165982,0.001533506,0.001331032,0.0008075244,0.0008690502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006112002,"about_ca_system_score_gemma":0.001272352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01175827,"about_ca_topic_score_gemma":0.01153976,"domain_scores_codex":[0.9991817,0.0001579347,0.00005059657,0.0002100676,0.0002748073,0.0001248534],"domain_scores_gemma":[0.9993401,0.0002411511,0.00006488061,0.0001045179,0.0002048882,0.00004431525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004006338,0.0001960771,0.00366019,0.0002266313,0.0001177032,0.0004135979,0.0003562424,0.440461,0.01530171,0.01124018,0.007771008,0.5198551],"study_design_scores_gemma":[0.00001018896,0.00002283724,0.0003058592,0.00000501558,0.000008884993,0.00007557611,0.00003161521,0.9942693,0.001888259,0.001810737,0.001556189,0.00001561625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0039764,0.000141033,0.9944014,0.00003954731,0.00004225371,0.00003495987,0.00004884283,0.0008235649,0.0004919958],"genre_scores_gemma":[0.3014203,0.0003139984,0.6951272,0.0001393661,0.00009839578,0.0002045812,0.0004996154,0.0003093324,0.001887269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01175827,"threshold_uncertainty_score":0.02337968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02692782536213188,"score_gpt":0.2319325244754255,"score_spread":0.2050046991132936,"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."}}