{"id":"W4200213365","doi":"10.23919/iccas52745.2021.9649791","title":"A Mobile Robotic Application of Naive Multi-directional Stitching with SIFT","year":2021,"lang":"en","type":"article","venue":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Image stitching; Computer science; Computer vision; Mobile robot; Artificial intelligence; Motion planning; Traverse; Robot; Scale-invariant feature transform; Point cloud; Feature extraction","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":[0.0002534906,0.0003894509,0.0003710908,0.0005445474,0.0003057061,0.0003351536,0.0006069139,0.0005844251,0.002063132],"category_scores_gemma":[0.0004115981,0.0002358251,0.0003608013,0.0005150263,0.0002720628,0.0005372277,0.0004665887,0.0003380352,0.0006770245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001976858,"about_ca_system_score_gemma":0.0003108487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002222676,"about_ca_topic_score_gemma":0.003012664,"domain_scores_codex":[0.9997626,0.00001657797,0.000009399609,0.00006261194,0.0001171496,0.00003163953],"domain_scores_gemma":[0.9999076,0.00001495782,0.00000862157,0.00003063251,0.00003096313,0.00000715844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000354945,0.000127791,0.00145789,0.0002355655,0.00007340325,0.0005674233,0.0002718751,0.0568546,0.2483484,0.005364738,0.003331028,0.6830124],"study_design_scores_gemma":[0.00006578926,0.0008266539,0.004978755,0.00003730087,0.00003835394,0.001523196,0.0001447596,0.881444,0.08920106,0.002352974,0.01931908,0.00006809425],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.102793,0.0004546588,0.8832577,0.0001402409,0.0001237889,0.0001855008,0.0001213082,0.00568578,0.007237991],"genre_scores_gemma":[0.412897,0.0001933002,0.5818235,0.00005065049,0.00002549883,0.00004646537,0.0002198252,0.0001197718,0.004624072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002222676,"threshold_uncertainty_score":0.00690186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775867043187599,"score_gpt":0.2460008238686668,"score_spread":0.2282421534367908,"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."}}