{"id":"W2243583397","doi":"10.1007/978-1-84882-985-5_30","title":"Particle Filtering with Range Data Association for Mobile Robot Localization in Environments with Repetitive Geometry","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in control and information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Odometry; Mobile robot; Patrolling; Computer vision; Robot; Artificial intelligence; Global Positioning System; Range (aeronautics); Computer science; Particle filter; Engineering; Simulation; Real-time computing; Geography; Kalman filter; Aerospace engineering","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":[0.000526947,0.0007244594,0.000933076,0.0005398425,0.0003610152,0.0006328078,0.001014513,0.001165739,0.002182997],"category_scores_gemma":[0.001554451,0.000633885,0.0007775838,0.001155403,0.0005275722,0.001204633,0.0008053576,0.001166197,0.001639299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858331,"about_ca_system_score_gemma":0.0003620307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00276,"about_ca_topic_score_gemma":0.002688213,"domain_scores_codex":[0.9995784,0.00009412206,0.00002801156,0.0001130102,0.000160272,0.00002614755],"domain_scores_gemma":[0.9995559,0.0002196163,0.00002962321,0.00009410656,0.00009166178,0.000009090002],"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":[0.0001888073,0.0001102927,0.0004304837,0.000287689,0.0001544682,0.0001498311,0.0001356978,0.2502505,0.02243335,0.02656393,0.006782384,0.6925125],"study_design_scores_gemma":[0.00001143299,0.00005021741,0.0002499408,0.00001051962,0.00002576622,0.00008410715,0.00000937923,0.9835057,0.004868018,0.006822667,0.004345569,0.00001683973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001066947,0.0002593225,0.9979827,0.00002977833,0.00006025287,0.000007153714,0.000008675449,0.0001878258,0.0003972361],"genre_scores_gemma":[0.09488875,0.001156347,0.8945915,0.0001115718,0.0002136208,0.0001115596,0.0001858038,0.0001348089,0.008606093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00276,"threshold_uncertainty_score":0.007302821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059727416811337,"score_gpt":0.2073921448776487,"score_spread":0.1967948707095354,"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."}}