{"id":"W4314946009","doi":"10.1109/cdc51059.2022.9992446","title":"Disturbance Observer and Depth Enhanced Visual-Inertial Navigation System For Multi-rotor MAVs: An Observability Analysis","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 61st Conference on Decision and Control (CDC)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Memorial University of Newfoundland","funders":"National Research Council","keywords":"Observability; Control theory (sociology); Drag; Filter (signal processing); Computer science; State observer; Observer (physics); Disturbance (geology); Inertial frame of reference; Nonlinear system; Simulation; Computer vision; Engineering; Artificial intelligence; Mathematics; Physics; Aerospace engineering; Geology; Control (management)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004083454,0.0002665199,0.0004998365,0.0001406699,0.0003473278,0.0001659377,0.0001703131,0.00009504702,0.00005906302],"category_scores_gemma":[0.0000617808,0.0002562868,0.0001282568,0.0003774627,0.00004203856,0.0001684906,0.00003034832,0.0001919405,0.00000257084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001402445,"about_ca_system_score_gemma":0.00002826103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005100945,"about_ca_topic_score_gemma":0.0001461531,"domain_scores_codex":[0.9981494,0.0001571552,0.0004879052,0.0005571321,0.0003788926,0.0002695322],"domain_scores_gemma":[0.9989867,0.0002088277,0.000110656,0.0003529853,0.0001667217,0.0001741295],"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":[0.002117147,0.0004770572,0.009063758,0.0003445265,0.0005839626,0.00002092031,0.0007065972,0.7895848,0.09068483,0.003498295,0.0001252861,0.1027929],"study_design_scores_gemma":[0.002625107,0.0003054463,0.0201336,0.00003280697,0.0001938876,0.000001788948,0.0003529474,0.9744684,0.00137048,0.00007656395,0.0001420992,0.0002969328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5649186,0.0001040179,0.4338261,0.00002450024,0.0003226936,0.0005659657,0.0001189184,0.00009898277,0.00002018779],"genre_scores_gemma":[0.9980853,0.0000292944,0.001137167,0.00007844108,0.00006281379,0.0003564994,0.0001570063,0.00003211258,0.00006140471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4331666,"threshold_uncertainty_score":0.9999889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03668278243191297,"score_gpt":0.2824474970588383,"score_spread":0.2457647146269253,"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."}}