{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002321364,0.0003616199,0.0003492118,0.0003517872,0.0001892387,0.0003504801,0.0002756216,0.0002625563,0.001136631],"category_scores_gemma":[0.000886907,0.0002556194,0.0003297617,0.000578133,0.0002563934,0.0004108818,0.0005115862,0.0005420902,0.0004791184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003425305,"about_ca_system_score_gemma":0.0006020287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00428097,"about_ca_topic_score_gemma":0.003894691,"domain_scores_codex":[0.9998062,0.00003835089,0.000006859934,0.00003341418,0.0001046677,0.00001053151],"domain_scores_gemma":[0.9998752,0.0000413571,0.00001313771,0.00001598035,0.00004878549,0.000005588541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004761308,0.00004826897,0.0005410095,0.0001486225,0.00006131519,0.00003015886,0.0001354933,0.4257301,0.01559513,0.01840858,0.006255423,0.5329983],"study_design_scores_gemma":[0.000007779558,0.00002444169,0.000425032,0.00001305527,0.000008675795,0.00001329472,0.00002094194,0.9814896,0.003701034,0.008441857,0.005846045,0.000008256457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003911986,0.0003496254,0.9941591,0.00007743197,0.00002390112,0.0000130454,0.00003394276,0.0003427338,0.001088308],"genre_scores_gemma":[0.2339591,0.001627462,0.7547402,0.00008315976,0.0001050326,0.0001596785,0.0005600633,0.0002226777,0.008542544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00428097,"threshold_uncertainty_score":0.008512139,"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."}}