{"id":"W2154343844","doi":"10.1109/robot.2008.4543554","title":"Dynamic visibility checking for vision-based motion planning","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Visibility; Frustum; Trajectory; Artificial intelligence; Motion planning; Path (computing); Field (mathematics); Position (finance); Field of view; Visual servoing; Algorithm; Image (mathematics); Robot; 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":[0.001050224,0.0005880609,0.0008422799,0.0009081207,0.0007250914,0.0008953772,0.001160018,0.000782328,0.001087846],"category_scores_gemma":[0.00671987,0.0005348481,0.0006177895,0.0006830723,0.001125911,0.001623185,0.00124917,0.0009948086,0.0001904139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009583452,"about_ca_system_score_gemma":0.001466576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007691788,"about_ca_topic_score_gemma":0.004689203,"domain_scores_codex":[0.9990361,0.0002301907,0.00005768377,0.0002533772,0.0003150907,0.0001075735],"domain_scores_gemma":[0.9970933,0.001940713,0.0003251544,0.0002965974,0.0002674884,0.00007668154],"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":[0.0002219894,0.00004421122,0.001268008,0.00009617337,0.00003408038,0.000134174,0.0001380594,0.8342965,0.008153769,0.02353026,0.0008708438,0.131212],"study_design_scores_gemma":[0.00001587783,0.00003224871,0.0001688209,0.000008800959,0.000006663758,0.00003214992,0.00001277785,0.9836687,0.003287849,0.01215007,0.0006060443,0.000009826123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01721477,0.0001735888,0.9808641,0.00005859207,0.00001640563,0.00002181027,0.00003001708,0.001001441,0.0006191995],"genre_scores_gemma":[0.6637187,0.0001710588,0.3350978,0.00003323412,0.00001956269,0.00006628235,0.000165105,0.0001370908,0.0005912337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007691788,"threshold_uncertainty_score":0.01529408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02909541334442284,"score_gpt":0.3384533338049612,"score_spread":0.3093579204605383,"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."}}