{"id":"W1896090928","doi":"10.1002/rob.21444","title":"Lighting‐invariant Visual Teach and Repeat Using Appearance‐based Lidar","year":2012,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Invariant (physics); Lidar; Mathematics; Remote sensing; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000369398,0.0004399715,0.0004834761,0.0007498172,0.0003261698,0.0005344123,0.001017528,0.0004255627,0.001117069],"category_scores_gemma":[0.00114448,0.000348638,0.0003581835,0.000537206,0.0003612195,0.0008848778,0.00101291,0.0004066704,0.0006272577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003043895,"about_ca_system_score_gemma":0.0004788262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002391803,"about_ca_topic_score_gemma":0.004122945,"domain_scores_codex":[0.9995275,0.00007233446,0.00001186981,0.0001026209,0.0002299214,0.00005571745],"domain_scores_gemma":[0.9994696,0.00008637503,0.0001269306,0.0001591247,0.0001167015,0.00004136012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003956606,0.0001815152,0.005893408,0.0001242556,0.00006507017,0.0002836102,0.0003352168,0.03376472,0.3340081,0.001327316,0.00211873,0.6215024],"study_design_scores_gemma":[0.0001304748,0.0009323809,0.01668635,0.00003121433,0.0001026825,0.00160196,0.0002692569,0.7016927,0.2669955,0.002438684,0.008932195,0.0001865062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3370917,0.000181346,0.6513152,0.0001410773,0.00005000268,0.000108504,0.0001676811,0.006035625,0.004908848],"genre_scores_gemma":[0.7853902,0.00008333186,0.2120155,0.00006404184,0.00002172279,0.00004839432,0.0001943822,0.0001386245,0.002043859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002391803,"threshold_uncertainty_score":0.004755795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747516781307993,"score_gpt":0.254006053084377,"score_spread":0.236530885271297,"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."}}