{"id":"W2153565898","doi":"10.1109/imtc.1997.604018","title":"3D data acquisition for indoor environment modeling using a compact active range sensor","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Computer science; Visualization; Range (aeronautics); Tilt sensor; Real-time computing; Computer vision; Remote sensing; Artificial intelligence; Engineering; Geography; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002369215,0.0005268616,0.0005217884,0.0005847971,0.0003298428,0.0006426628,0.0005637998,0.0003413419,0.002134999],"category_scores_gemma":[0.0008930473,0.0003362342,0.0003636937,0.0004607541,0.0002422102,0.0009770138,0.0006540805,0.0003986546,0.0007465115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002607534,"about_ca_system_score_gemma":0.0004589081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001632723,"about_ca_topic_score_gemma":0.003895998,"domain_scores_codex":[0.9996922,0.00004498305,0.00001060704,0.00004775269,0.0001884557,0.00001601213],"domain_scores_gemma":[0.9997228,0.00008107311,0.00003581331,0.00007594797,0.00006927115,0.00001509226],"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":[0.0002458399,0.0001293737,0.003758385,0.0004095299,0.00004800268,0.0003056456,0.0006266342,0.1360002,0.3303341,0.01225631,0.003176483,0.5127096],"study_design_scores_gemma":[0.00003143794,0.0002612888,0.004431997,0.00004407819,0.00004599604,0.0006636268,0.0002761905,0.8112264,0.1533229,0.003039554,0.02657717,0.00007933227],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01857505,0.00009597094,0.9782249,0.00005183672,0.00002114918,0.0000358636,0.0001063649,0.001167062,0.001721822],"genre_scores_gemma":[0.3140363,0.0004120727,0.6831798,0.00004795268,0.00002310324,0.0001604426,0.0004000077,0.0001495639,0.001590776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002134999,"threshold_uncertainty_score":0.007142305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09616600290835309,"score_gpt":0.2511431528244553,"score_spread":0.1549771499161022,"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."}}