{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009119653,0.0006444413,0.0004181554,0.0005152671,0.0002623583,0.0006798607,0.0004343538,0.0005290638,0.0006933322],"category_scores_gemma":[0.004739281,0.0003796034,0.0006603505,0.0004747502,0.0008724731,0.001779675,0.0009993331,0.000658426,0.0001712078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004616816,"about_ca_system_score_gemma":0.0006758574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004206243,"about_ca_topic_score_gemma":0.003410088,"domain_scores_codex":[0.9994569,0.0001383139,0.00002508089,0.0001040209,0.0002296996,0.00004593142],"domain_scores_gemma":[0.9988343,0.0006289456,0.0002292673,0.0001411901,0.0001424229,0.00002394803],"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":[0.0002303778,0.00005439408,0.00805446,0.000252157,0.0001486825,0.000349414,0.0003281597,0.6294788,0.03278304,0.1030501,0.0005732367,0.2246972],"study_design_scores_gemma":[0.000009561501,0.0001079664,0.003222311,0.00001838228,0.00002921601,0.00009677555,0.00004437126,0.9631833,0.005867596,0.02538926,0.002005231,0.00002589308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01210673,0.0001352588,0.9869811,0.00007549625,0.00001294152,0.000007300685,0.00001956528,0.00008882696,0.0005728863],"genre_scores_gemma":[0.8414454,0.0005385967,0.1565312,0.00005634422,0.00006411582,0.00006079946,0.0001003569,0.00004769547,0.001155424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004206243,"threshold_uncertainty_score":0.008363485,"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."}}