{"id":"W2033686935","doi":"10.1109/iros.2006.281899","title":"Rover Localization through 3D Terrain Registration in Natural Environments","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"Canadian Space Agency; European Space Agency","keywords":"Computer science; Computer vision; Terrain; Artificial intelligence; Matching (statistics); Image registration; Point cloud; Lidar; Point set registration; Remote sensing; Point (geometry); Geography; Image (mathematics); Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003763909,0.00008844493,0.00006844354,0.0000375876,0.00002215687,0.00002487068,0.00003197592,0.00006201226,0.00005989576],"category_scores_gemma":[0.000004917554,0.00008827464,0.00001663106,0.0001102393,0.0000134798,0.0001798451,0.000004437679,0.00005651137,0.00002292034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009322756,"about_ca_system_score_gemma":0.000003105155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002342384,"about_ca_topic_score_gemma":0.0003796397,"domain_scores_codex":[0.9994422,0.00001260579,0.0001924446,0.000107794,0.0001206474,0.0001243073],"domain_scores_gemma":[0.9998573,0.000008268227,0.00001764425,0.0001012083,0.000004605978,0.00001095295],"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":[0.000001902402,0.00001715796,0.00144752,0.00000822843,0.00000219369,0.000002064936,0.00004574217,0.9910297,0.002567837,0.002841204,0.001710668,0.000325825],"study_design_scores_gemma":[0.0002673792,0.000007644461,0.005773033,0.000009064208,0.000002849617,9.642538e-7,0.00001566215,0.9826053,0.003908813,0.0006328177,0.00665278,0.0001237328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03157128,0.00008481699,0.9499256,0.0000566799,0.000186241,0.0001259057,0.000001294942,0.00009878362,0.01794938],"genre_scores_gemma":[0.9963701,0.00001930828,0.002356106,0.00009449972,0.00005560616,0.00000350174,0.0001622904,0.00001744136,0.0009211573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9647988,"threshold_uncertainty_score":0.3599735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005049895127221142,"score_gpt":0.1871460455982502,"score_spread":0.182096150471029,"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."}}