{"id":"W2075500716","doi":"10.1007/s11263-006-0021-0","title":"A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Simultaneous localization and mapping; Particle filter; Computer science; Odometry; Computer vision; Artificial intelligence; Pose; Scalability; Filter (signal processing); Range (aeronautics); Monocular vision; Robotics; Mobile robot; Robot; 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":[0.002532878,0.0007358372,0.0008770157,0.0006700037,0.0003465893,0.001512683,0.000988273,0.001498755,0.001814455],"category_scores_gemma":[0.009598481,0.0007650498,0.000930735,0.001119247,0.001004623,0.002258663,0.0008517583,0.001480185,0.0007000713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006781746,"about_ca_system_score_gemma":0.001483451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004888821,"about_ca_topic_score_gemma":0.002915798,"domain_scores_codex":[0.9990284,0.0003146199,0.0000569769,0.0001306499,0.000404363,0.00006500025],"domain_scores_gemma":[0.9963257,0.0025669,0.0001782852,0.0002471108,0.0006092513,0.00007268386],"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":[0.0002472901,0.00007676103,0.001010866,0.0005361424,0.0002290812,0.0002528027,0.0001881565,0.6292136,0.0156053,0.1696143,0.002925024,0.1801007],"study_design_scores_gemma":[0.000007112417,0.00005213243,0.0003126449,0.00001578342,0.00001812597,0.000121163,0.0000112264,0.9870054,0.001914036,0.008487321,0.0020386,0.00001641899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002587959,0.0006089991,0.9957591,0.00009944284,0.00006007013,0.000007434154,0.000008437073,0.0000579229,0.0008106038],"genre_scores_gemma":[0.326414,0.003759399,0.6591269,0.0002704888,0.0004379266,0.00008209233,0.000151057,0.0002895345,0.009468625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004888821,"threshold_uncertainty_score":0.01339531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248595908966754,"score_gpt":0.2817521895487849,"score_spread":0.2692662304591174,"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."}}