{"id":"W3000338907","doi":"10.1007/s10514-019-09897-6","title":"Joint optimization based on direct sparse stereo visual-inertial odometry","year":2020,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Odometry; Inertial measurement unit; Robustness (evolution); Stereo camera; Visual odometry; Bundle adjustment; Simultaneous localization and mapping; Pixel; Image (mathematics); Robot; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000294459,0.0007316076,0.00106688,0.0004718785,0.0003020661,0.0005909717,0.0006370282,0.0005421514,0.001968766],"category_scores_gemma":[0.001237668,0.0005827042,0.0005993907,0.0008520043,0.0004929498,0.0008438142,0.001051781,0.0006021056,0.0005946085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003695847,"about_ca_system_score_gemma":0.001505539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009896413,"about_ca_topic_score_gemma":0.01111208,"domain_scores_codex":[0.9997048,0.00004309191,0.00001347046,0.00006450788,0.0001388776,0.00003527661],"domain_scores_gemma":[0.9997186,0.00009889235,0.00004040168,0.00003732341,0.00008453211,0.00002025589],"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":[0.00008925422,0.00005759482,0.000526567,0.00009358481,0.00006022396,0.00003673862,0.00004970693,0.8522426,0.005893467,0.007447323,0.002352501,0.1311505],"study_design_scores_gemma":[0.000007467957,0.00002132487,0.0001823483,0.000003300655,0.000005900157,0.00001227367,0.000005974138,0.9964641,0.0006283687,0.002252704,0.000411207,0.000005103225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006608197,0.00008653058,0.9917763,0.00006176405,0.00002904269,0.00001496617,0.00005061788,0.0002576014,0.00111487],"genre_scores_gemma":[0.5957441,0.000329705,0.3968079,0.0001264232,0.00008792175,0.000185346,0.0005470169,0.0001931945,0.005978382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009896413,"threshold_uncertainty_score":0.01967764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02150829217883478,"score_gpt":0.2123589575417816,"score_spread":0.1908506653629468,"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."}}