{"id":"W2281607193","doi":"10.5194/isprsarchives-xl-1-w4-183-2015","title":"THE PERFORMANCE ANALYSIS OF AN INDOOR MOBILE MAPPING SYSTEM WITH RGB-D SENSOR","year":2015,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Mobile mapping; Computer science; Inertial measurement unit; Global Positioning System; Simultaneous localization and mapping; Lidar; Real-time computing; RGB color model; Photogrammetry; Artificial intelligence; Mobile robot; Computer vision; Floor plan; Robot; Inertial navigation system; Point cloud; Remote sensing; Engineering; Orientation (vector space); Geography; Telecommunications","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":[0.000255634,0.0004319435,0.0003588395,0.0003833296,0.0003296071,0.000387331,0.0003714991,0.0003026579,0.004109248],"category_scores_gemma":[0.0004780919,0.000112247,0.0002625726,0.0003143804,0.0001544153,0.0003959544,0.0002724051,0.0001585904,0.0007875076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978632,"about_ca_system_score_gemma":0.0002942914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003562337,"about_ca_topic_score_gemma":0.001608976,"domain_scores_codex":[0.9996175,0.00006259688,0.00001477983,0.00007249305,0.0001644154,0.00006825059],"domain_scores_gemma":[0.9996854,0.00006448878,0.00003181063,0.00004158582,0.0001541783,0.00002252983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003552213,0.0004599045,0.030596,0.001138534,0.0003399464,0.001002215,0.0006253335,0.3393924,0.3027858,0.00279812,0.006244936,0.3110646],"study_design_scores_gemma":[0.00005728795,0.001892988,0.03515411,0.0000273741,0.0001260907,0.0004926589,0.0002602677,0.8287269,0.1285012,0.0003847289,0.004309561,0.00006675895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9034584,0.0005342865,0.0808251,0.0001932345,0.0001299755,0.00007618263,0.0003130327,0.002475053,0.01199485],"genre_scores_gemma":[0.9967175,0.00004124899,0.001935507,0.00001596595,0.00000357457,0.00001474306,0.00007777759,0.00001279918,0.001180877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004109248,"threshold_uncertainty_score":0.01374674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508947268487771,"score_gpt":0.2309336611939133,"score_spread":0.2158441885090356,"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."}}