{"id":"W2030881513","doi":"10.1109/robot.2010.5509767","title":"Global rover localization by matching lidar and orbital 3D maps","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Odometry; Lidar; Visual odometry; Computer vision; Artificial intelligence; Computer science; Orientation (vector space); Terrain; Traverse; Remote sensing; Matching (statistics); Geology; Mars Exploration Program; Mars rover; Geodesy; Mobile robot; Geography; Robot; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002468692,0.0004787082,0.0004669357,0.001448766,0.0002184615,0.0008303504,0.0005441189,0.0003952137,0.0008802233],"category_scores_gemma":[0.00113105,0.000285699,0.0002887837,0.001081778,0.0002255434,0.0008162293,0.000944246,0.0002661191,0.0007010304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001994148,"about_ca_system_score_gemma":0.0004507006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002917793,"about_ca_topic_score_gemma":0.004593465,"domain_scores_codex":[0.9997287,0.00004064629,0.00001160059,0.00009096054,0.00009622386,0.00003185117],"domain_scores_gemma":[0.9997841,0.00003463005,0.000045772,0.00004920041,0.00007525879,0.00001119146],"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":[0.0001271832,0.00006108162,0.009163381,0.00008452754,0.0000761838,0.0001563035,0.0002729805,0.08411578,0.06162766,0.002311267,0.001500531,0.8405032],"study_design_scores_gemma":[0.00008060824,0.0003372179,0.02641173,0.00005371612,0.0001311654,0.0007658791,0.0006878596,0.8873084,0.06341751,0.007599229,0.01312698,0.00007962294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09548742,0.000185311,0.9008878,0.00005186493,0.00001811669,0.00005355214,0.00009891806,0.001790691,0.001426352],"genre_scores_gemma":[0.618009,0.0001948194,0.3797425,0.00003801873,0.00001711967,0.00007853037,0.0004113913,0.0001082321,0.001400387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002917793,"threshold_uncertainty_score":0.005801558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00281894782475587,"score_gpt":0.1854510823236996,"score_spread":0.1826321344989437,"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."}}