{"id":"W2064811848","doi":"10.1109/iros.2010.5650359","title":"Integrating IMU and landmark sensors for 3D SLAM and the observability analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Observability; Inertial measurement unit; Landmark; Simultaneous localization and mapping; Computer vision; Kalman filter; Artificial intelligence; Computer science; Noise (video); Extended Kalman filter; Covariance; Line (geometry); Filter (signal processing); Mathematics; Mobile robot; Robot; Image (mathematics)","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":[0.000268958,0.00007204612,0.0001339541,0.0000313429,0.00006750746,0.00005230971,0.00002993113,0.00004673859,0.00001655817],"category_scores_gemma":[0.0001070069,0.00004319974,0.00004043205,0.0001246639,0.0000522822,0.00003447158,0.000007052785,0.00008286832,3.356596e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004053761,"about_ca_system_score_gemma":0.000002139689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008485429,"about_ca_topic_score_gemma":0.001035961,"domain_scores_codex":[0.999632,0.00001575437,0.0001213128,0.00009975029,0.00004251194,0.0000886809],"domain_scores_gemma":[0.9995077,0.000281225,0.00001293692,0.0001295583,0.00003777522,0.00003083665],"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":[0.000135215,0.00004357681,0.1956225,0.000387135,0.001203337,0.000001622126,0.003809585,0.6847919,0.009004839,0.07150079,0.0006624748,0.03283698],"study_design_scores_gemma":[0.0003150888,0.000005695039,0.006691526,0.000001593085,0.0001085162,6.064821e-7,0.000101673,0.9917089,0.0002301434,0.0004278324,0.0003446657,0.00006378128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8313956,0.00002410909,0.1673385,0.0001756744,0.00006488219,0.0001426809,0.000004125347,0.00005529364,0.0007991661],"genre_scores_gemma":[0.9799941,0.00001799536,0.01981976,0.0000362055,0.00003209136,0.000006678745,0.000009708974,0.000008115954,0.00007538468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.306917,"threshold_uncertainty_score":0.1761634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005190334281385091,"score_gpt":0.1986733683164276,"score_spread":0.1934830340350425,"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."}}