{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009737034,0.0001070233,0.0001015975,0.00006341567,0.00006828057,0.00005794452,0.00003949405,0.00008332337,0.00001560002],"category_scores_gemma":[0.00002752164,0.0001002631,0.00003881029,0.00007863816,0.000008397322,0.0000890459,0.000006923888,0.00007187654,0.00000451495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001427462,"about_ca_system_score_gemma":0.000003727229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009139636,"about_ca_topic_score_gemma":0.00004717365,"domain_scores_codex":[0.9994873,0.000006663218,0.0001406151,0.0001321209,0.0000814884,0.000151772],"domain_scores_gemma":[0.9997575,0.00004252302,0.00001383844,0.0001190979,0.00001949835,0.00004757275],"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":[0.00003207541,0.00009237874,0.06058255,0.0005839681,0.0001407865,0.00001858658,0.002375876,0.8550612,0.01576594,0.01884661,0.003193315,0.04330667],"study_design_scores_gemma":[0.0004387241,0.00003424644,0.002498227,0.00003544092,0.00001013634,0.000001320976,0.00005082714,0.992931,0.00100376,0.0002301245,0.002601201,0.0001649746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.233554,0.00001867451,0.7643764,0.00005367434,0.0003957044,0.0002037563,0.000002638072,0.0001489911,0.00124621],"genre_scores_gemma":[0.9949322,0.000006879046,0.004557585,0.0001878708,0.00009760638,0.00001252149,0.00003723071,0.00003392788,0.0001341598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7613782,"threshold_uncertainty_score":0.4088612,"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."}}