{"id":"W4403004482","doi":"10.23977/jeis.2024.090310","title":"A SLAM method based on deep learning","year":2024,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Deep learning; Computer science; Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004416115,0.001011424,0.001204736,0.0009534614,0.0005623233,0.0007000917,0.001373118,0.0008495512,0.002797392],"category_scores_gemma":[0.000815793,0.0006831276,0.0009416524,0.001315307,0.0004732571,0.001158993,0.001656482,0.001887151,0.001349417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000735637,"about_ca_system_score_gemma":0.001519577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006441379,"about_ca_topic_score_gemma":0.008161392,"domain_scores_codex":[0.9995334,0.00004530298,0.00002036608,0.0001358091,0.0002023996,0.00006262284],"domain_scores_gemma":[0.9997914,0.00003632896,0.00002285107,0.00004762162,0.00008120466,0.00002053653],"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":[0.00009964059,0.00006968011,0.0005435776,0.0001433427,0.0001074692,0.00007305793,0.00006722315,0.2609481,0.02243351,0.01343855,0.008536935,0.6935389],"study_design_scores_gemma":[0.00001207504,0.00002585491,0.0001470568,0.000006912112,0.0000085761,0.00003401307,0.000006153578,0.9895336,0.003116685,0.003852382,0.003244712,0.00001199985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001692988,0.00009354848,0.9961872,0.00005626154,0.00006937666,0.00002495869,0.00005275377,0.001132795,0.0006901679],"genre_scores_gemma":[0.1948514,0.0003632657,0.7951897,0.0002623236,0.0001485978,0.0002281153,0.0006288965,0.0004080086,0.007919716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006441379,"threshold_uncertainty_score":0.01280779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004440479754016132,"score_gpt":0.2396434101739845,"score_spread":0.2352029304199684,"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."}}