{"id":"W2123726238","doi":"10.1109/robot.2006.1641930","title":"σSLAM: stereo vision SLAM using the Rao-Blackwellised particle filter and a novel mixture proposal distribution","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Computer vision; Artificial intelligence; Particle filter; Simultaneous localization and mapping; Landmark; Computer science; Scale-invariant feature transform; Stereopsis; Filter (signal processing); Feature extraction; Mobile robot; Robot","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002252873,0.0007226146,0.001050564,0.0008096918,0.0004218425,0.001206795,0.001959268,0.001390386,0.0014476],"category_scores_gemma":[0.005018287,0.0007427368,0.0009701775,0.001020789,0.0008855235,0.001912243,0.002031031,0.001284117,0.001087277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006020092,"about_ca_system_score_gemma":0.001137686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003330969,"about_ca_topic_score_gemma":0.002770154,"domain_scores_codex":[0.9989262,0.0002992764,0.00004688458,0.0001793084,0.0004748914,0.00007336686],"domain_scores_gemma":[0.9983871,0.0007318485,0.0001689304,0.0002791827,0.0003519662,0.00008108859],"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":[0.0002845963,0.0001170235,0.001252598,0.0001447511,0.0001489841,0.0001216738,0.0001175221,0.6536103,0.007491685,0.02191117,0.003263527,0.3115362],"study_design_scores_gemma":[0.0000142682,0.0000283665,0.00007683552,0.000003714487,0.000005395272,0.00001945881,0.00000297117,0.9953771,0.0009841825,0.002618186,0.0008622106,0.000007284621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002235346,0.00005026376,0.9970428,0.00004147993,0.00002144926,0.00001007881,0.00000986009,0.0003884406,0.0002001648],"genre_scores_gemma":[0.2600146,0.0002551907,0.7362183,0.0001678072,0.0001220847,0.0001739433,0.0002168923,0.0002594418,0.002571676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003330969,"threshold_uncertainty_score":0.01191449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009921168110797214,"score_gpt":0.21144861772101,"score_spread":0.2015274496102128,"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."}}