{"id":"W4396764617","doi":"10.5194/isprs-annals-x-1-2024-283-2024","title":"M-GCLO: Multiple Ground Constrained LiDAR Odometry","year":2024,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Odometry; Lidar; Remote sensing; Computer science; Artificial intelligence; Computer vision; Environmental science; Geography; Robot; Mobile robot","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":[],"consensus_categories":[],"category_scores_codex":[0.0005615274,0.0001406744,0.0001646027,0.0003074247,0.0002128959,0.0004137147,0.0001344307,0.00007633253,0.000007882365],"category_scores_gemma":[0.0002260929,0.000101628,0.00009359774,0.000937185,0.0003270086,0.0002814168,0.00003490158,0.0001271299,0.000006001354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001163011,"about_ca_system_score_gemma":0.00003783235,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02046235,"about_ca_topic_score_gemma":0.001351016,"domain_scores_codex":[0.9988683,0.00003515691,0.0003935253,0.0001155845,0.0003548388,0.0002326246],"domain_scores_gemma":[0.9994249,0.0001645921,0.00007390241,0.0001486033,0.0001174745,0.00007052778],"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":[0.000004720503,0.000002805664,0.00004040282,0.0001100704,0.00002156599,6.307774e-7,0.0004886266,0.03819912,0.001182337,0.00003500873,0.0003321678,0.9595826],"study_design_scores_gemma":[0.00009009606,0.00004305732,0.0003924948,0.0001126392,0.000009714357,0.00001493014,0.0003509021,0.9791499,0.01489758,0.0003933795,0.00441526,0.0001299812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1006359,0.0003171568,0.8944434,0.0006085665,0.001085735,0.0001863671,0.00002307233,0.0001597169,0.002540087],"genre_scores_gemma":[0.9983201,0.0001626237,0.001194208,0.0002399003,0.00005173875,7.836422e-8,0.000007764695,0.000008353246,0.00001524602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9594526,"threshold_uncertainty_score":0.9860605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03449280682021257,"score_gpt":0.2734907399817534,"score_spread":0.2389979331615409,"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."}}