{"id":"W4390189964","doi":"10.1109/iccvw60793.2023.00486","title":"DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CODE; Ministry of Education","keywords":"Odometry; Lidar; Computer science; Artificial intelligence; Remote sensing; Computer vision; Geology; Mobile robot; Robot","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005299526,0.001183654,0.001036003,0.0007802481,0.0004315363,0.0009402955,0.002635042,0.0009574381,0.003466117],"category_scores_gemma":[0.001557056,0.0006990443,0.0009082719,0.0008975258,0.0007090393,0.001637688,0.002262785,0.001870559,0.001555966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009377862,"about_ca_system_score_gemma":0.001697795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01681346,"about_ca_topic_score_gemma":0.02948941,"domain_scores_codex":[0.999701,0.00003691687,0.00001407426,0.0001054369,0.00009675507,0.00004581655],"domain_scores_gemma":[0.999709,0.00005887312,0.00004176055,0.0001018677,0.00006286836,0.00002563442],"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":[0.0001815764,0.0001155347,0.00196557,0.0001926156,0.0001629211,0.0001518415,0.000127925,0.5622118,0.005038766,0.01255751,0.01723714,0.4000567],"study_design_scores_gemma":[0.00001248839,0.00001750055,0.0001532959,0.000009748851,0.000005598336,0.00002268049,0.000009787342,0.9912527,0.001014919,0.005540784,0.001952851,0.0000076246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01209238,0.0003867407,0.9764141,0.000255585,0.0001021717,0.00006727539,0.000949503,0.0080401,0.001692126],"genre_scores_gemma":[0.5170813,0.0004797556,0.4646635,0.0004435356,0.00012586,0.0002497059,0.007511665,0.0009560327,0.00848866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01681346,"threshold_uncertainty_score":0.03343117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02408488043965996,"score_gpt":0.2452121686723089,"score_spread":0.221127288232649,"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."}}