{"id":"W2010056799","doi":"10.2316/journal.206.2013.3.206-3792","title":"FEATURE-BASED 3D OUTDOOR SLAM WITH LOCAL FILTERS","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extended Kalman filter; Simultaneous localization and mapping; Kalman filter; Computer vision; Feature (linguistics); Artificial intelligence; Computer science; Lidar; Remote sensing; Geography; Robot; Mobile robot","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000304889,0.0006380891,0.0009056182,0.00069299,0.0003826664,0.0006276233,0.0008778588,0.0006174819,0.001128024],"category_scores_gemma":[0.0008108898,0.0004412304,0.000768884,0.001271221,0.0003201823,0.001165663,0.000916673,0.000747126,0.0009848775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921584,"about_ca_system_score_gemma":0.0005632508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003865243,"about_ca_topic_score_gemma":0.005470185,"domain_scores_codex":[0.9995314,0.0000455709,0.00002036953,0.0001386796,0.0002145363,0.00004937658],"domain_scores_gemma":[0.9997216,0.00005357429,0.00004602269,0.00007371722,0.00009013909,0.00001501071],"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":[0.0002533579,0.00008678527,0.001525928,0.0002427997,0.0001740958,0.00015961,0.0001892795,0.1290221,0.1082041,0.003790525,0.002912937,0.7534386],"study_design_scores_gemma":[0.00003287088,0.0001492498,0.003198585,0.00002502878,0.00006230122,0.0002363859,0.00004823015,0.9464321,0.03809151,0.002948519,0.008720664,0.00005467215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005856441,0.0001125917,0.9926372,0.00001507709,0.00003139999,0.00001138673,0.00003986733,0.0009012246,0.0003948867],"genre_scores_gemma":[0.3769608,0.0003199464,0.6187642,0.00007678937,0.00008905539,0.0001136379,0.0004580505,0.0002117757,0.003005753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003865243,"threshold_uncertainty_score":0.007685483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004429338821219532,"score_gpt":0.1923214344774815,"score_spread":0.187892095656262,"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."}}