{"id":"W2998691290","doi":"10.22215/etd/2019-13841","title":"Sparse Stereo Visual Odometry with Local Non-Linear Least-Squares Optimization for Navigation of Autonomous Vehicles","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Visual odometry; Computer vision; Artificial intelligence; Bundle adjustment; Computer science; Benchmark (surveying); Odometry; Frame (networking); Stereo cameras; Stereopsis; Set (abstract data type); Mobile robot; Robot; Geography; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00008878584,0.0003239015,0.0004284082,0.0002782737,0.00004704878,0.00003964273,0.000109318,0.0003704353,0.00004881619],"category_scores_gemma":[0.00001283174,0.0003033489,0.00009648292,0.0002743754,0.00002613267,0.0001506615,0.000005541113,0.0001506827,0.00001170112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000940221,"about_ca_system_score_gemma":0.00008909422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000045465,"about_ca_topic_score_gemma":0.00003587495,"domain_scores_codex":[0.998711,0.00001560397,0.0004941793,0.0002813924,0.0002763685,0.0002214638],"domain_scores_gemma":[0.9991326,0.00007205725,0.0001850886,0.0002038914,0.0003471736,0.00005914633],"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":[0.0001740518,0.00004998286,0.00009297607,0.001437332,0.00008933842,0.000001160905,0.0001977859,0.9917405,0.00067728,0.0001493578,0.00007515737,0.005315079],"study_design_scores_gemma":[0.0007708517,0.0003312948,0.0001848308,0.0003591049,0.000110369,0.000001660623,0.0007937278,0.9721134,0.02492946,0.000007795734,0.00004324096,0.0003542815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1934378,0.00006521082,0.8046516,0.000004102939,0.0004027695,0.0006955246,0.0000305294,0.0001266137,0.000585783],"genre_scores_gemma":[0.9696068,0.00002543594,0.0204466,0.00001071219,0.0000952615,0.00003666929,0.00851114,0.0001553476,0.00111203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.784205,"threshold_uncertainty_score":0.9999419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0091038490194002,"score_gpt":0.2434956089992467,"score_spread":0.2343917599798465,"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."}}