{"id":"W2282557618","doi":"10.5220/0005535802670274","title":"HybridSLAM: A Robust Algorithm for Simultaneous Localization and Mapping","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Robustness (evolution); Computer science; Algorithm; Process (computing); Fusion; Simultaneous localization and mapping; Sensor fusion; Artificial intelligence; Path (computing); Mobile robot; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263783,0.000817821,0.0008347936,0.0008916793,0.0004026457,0.0009046174,0.001971193,0.001288832,0.002499528],"category_scores_gemma":[0.003360289,0.0005506434,0.0006531741,0.0008033744,0.0008988776,0.002139712,0.002598717,0.001430064,0.0009327431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005595,"about_ca_system_score_gemma":0.0008399163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001883512,"about_ca_topic_score_gemma":0.001654728,"domain_scores_codex":[0.9990168,0.000292004,0.00003896886,0.0001956468,0.0003828541,0.0000737176],"domain_scores_gemma":[0.9988248,0.00059662,0.0001173988,0.0001826944,0.0002414337,0.00003703028],"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":[0.0002956748,0.00006095738,0.0005699662,0.0002160342,0.0001324519,0.0001559698,0.0002244005,0.4643148,0.01597995,0.03062584,0.003819519,0.4836044],"study_design_scores_gemma":[0.00001631123,0.0000671743,0.0001068511,0.000009676286,0.00001031648,0.00007011785,0.00002274106,0.9853139,0.004244609,0.006486147,0.003634305,0.00001778608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001630583,0.00006424687,0.9975256,0.00003476044,0.00001593478,0.000008394948,0.0000106208,0.0004401043,0.0002697586],"genre_scores_gemma":[0.1786511,0.0001747674,0.8176404,0.0001136739,0.00006885207,0.0001443786,0.0001449767,0.0003042425,0.002757618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002499528,"threshold_uncertainty_score":0.008361757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536030064920578,"score_gpt":0.2111836759703959,"score_spread":0.1858233753211901,"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."}}