{"id":"W2003912216","doi":"10.1109/robot.2010.5509133","title":"Stereo mapping and localization for long-range path following on rough terrain","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Computer science; Visual odometry; Computer vision; Artificial intelligence; Robustness (evolution); Terrain; Stereo cameras; Pipeline (software); Simultaneous localization and mapping; Stereopsis; Mobile robot; Robot; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001761921,0.0002032409,0.0001659369,0.0003293617,0.000248729,0.0002582282,0.0002566215,0.0002460796,0.002456897],"category_scores_gemma":[0.0008573344,0.0001145994,0.0001200014,0.0002928852,0.0003051574,0.0004166636,0.0003546845,0.0002184646,0.0004613415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002354979,"about_ca_system_score_gemma":0.0005303225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00347251,"about_ca_topic_score_gemma":0.006342025,"domain_scores_codex":[0.9999123,0.00002072143,0.000002523997,0.00001030551,0.00004535933,0.000008745204],"domain_scores_gemma":[0.9998251,0.0000565115,0.00002496991,0.00003907462,0.00004392013,0.00001060716],"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":[0.000113988,0.00005077944,0.002198292,0.0001435585,0.00002739173,0.0001275488,0.000239065,0.1795786,0.06228217,0.01523334,0.003452249,0.736553],"study_design_scores_gemma":[0.00002338041,0.00009842939,0.002368574,0.00001122823,0.00001226911,0.000186944,0.0001108494,0.9586436,0.02143186,0.0110106,0.00608409,0.00001827104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04996442,0.0002108458,0.9456655,0.00009403952,0.00002424076,0.00002539729,0.00004564285,0.001572203,0.002397694],"genre_scores_gemma":[0.6726586,0.0002383935,0.3244002,0.00004177081,0.00001878749,0.00005452801,0.0001285034,0.0000872704,0.00237203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00347251,"threshold_uncertainty_score":0.008219182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109758673846947,"score_gpt":0.2151519553046934,"score_spread":0.2041760879199987,"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."}}