{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003415123,0.0002904553,0.0003946567,0.0008309203,0.0003190086,0.0006313575,0.0004978938,0.0004859746,0.0005483502],"category_scores_gemma":[0.00116098,0.0002454879,0.0003087973,0.0008111217,0.0005475515,0.001031935,0.0009230306,0.0002874772,0.0004277305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002266787,"about_ca_system_score_gemma":0.000344297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002473503,"about_ca_topic_score_gemma":0.002600882,"domain_scores_codex":[0.9995967,0.0001079097,0.00001659806,0.0001052583,0.0001299365,0.00004369959],"domain_scores_gemma":[0.9997569,0.00004627607,0.00006118685,0.00008208057,0.00004154463,0.00001202618],"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":[0.0002408653,0.00008867937,0.007531191,0.0001018513,0.00009447707,0.000577215,0.0004732359,0.3553241,0.1414235,0.008026887,0.001433536,0.4846845],"study_design_scores_gemma":[0.0000312996,0.0002174036,0.01006885,0.0000152073,0.00003917233,0.0008351803,0.0003242774,0.9057342,0.06768055,0.007234817,0.007772219,0.00004677434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1288306,0.0001792687,0.8682404,0.00008172417,0.00001564133,0.00003062159,0.00004239107,0.001365084,0.001214319],"genre_scores_gemma":[0.7502037,0.0002029039,0.248234,0.0000341376,0.00001209981,0.00003309919,0.0001235629,0.00007002876,0.001086518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002473503,"threshold_uncertainty_score":0.004918158,"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."}}