{"id":"W3110341679","doi":"10.1109/ccece47787.2020.9255714","title":"Multimodality Weight and Score Fusion for SLAM","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Trajectory; Simultaneous localization and mapping; Artificial intelligence; Computer science; Convolutional neural network; Feature (linguistics); Fusion; Deep learning; Computer vision; Robot; Layer (electronics); Sensor fusion; Pattern recognition (psychology); Mobile robot","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001988222,0.00004936437,0.00005857885,0.000008954042,0.00002442428,0.0000131733,0.00002039801,0.00003134231,0.00002993337],"category_scores_gemma":[0.00001391204,0.00004297625,0.00001513512,0.00003774383,0.00000601145,0.00002944749,0.000007089605,0.00002329995,0.000004433076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005057691,"about_ca_system_score_gemma":0.000002051191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004968341,"about_ca_topic_score_gemma":0.000004164976,"domain_scores_codex":[0.9997491,0.000002911886,0.00007020035,0.00007508692,0.00003562356,0.00006706917],"domain_scores_gemma":[0.999869,0.00001589759,0.000004730024,0.00004273052,0.00001660727,0.00005103435],"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":[0.00005137821,0.00003090667,0.006557749,0.0007291391,0.00004679411,0.000003684645,0.0008652858,0.8687223,0.04799392,0.02265231,0.01484837,0.03749822],"study_design_scores_gemma":[0.0002389945,0.00001976834,0.0009051478,0.000003804895,0.000004638512,1.807732e-7,0.00001054369,0.9898344,0.005345596,0.0001267557,0.003446375,0.00006374787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1056632,0.00006153407,0.8916762,0.0006516777,0.00008208174,0.0001626263,0.000005630348,0.0001781317,0.001518897],"genre_scores_gemma":[0.9896104,0.00003074005,0.009984049,0.0002311869,0.00006997037,0.000003155276,0.00001756336,0.00001194396,0.00004097305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8839473,"threshold_uncertainty_score":0.1752521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02326866017705633,"score_gpt":0.2039325026825306,"score_spread":0.1806638425054743,"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."}}