{"id":"W2266469970","doi":"10.1007/978-3-642-40686-7_22","title":"Lighting-Invariant Visual Odometry using Lidar Intensity Imagery and Pose Interpolation","year":2013,"lang":"en","type":"book-chapter","venue":"Springer tracts in advanced robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Lidar; Computer science; Robustness (evolution); Visual odometry; Remote sensing; Geography; Robot","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":[0.0002072395,0.0007031846,0.0007489384,0.00100371,0.0002764321,0.000996649,0.001161363,0.0003918729,0.004440493],"category_scores_gemma":[0.0006158923,0.0006395638,0.0009027984,0.001852372,0.000400223,0.0009966452,0.001296118,0.000728564,0.004159691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002511384,"about_ca_system_score_gemma":0.000599331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002513381,"about_ca_topic_score_gemma":0.003972964,"domain_scores_codex":[0.9996945,0.00002082283,0.00001205356,0.00008457837,0.0001601667,0.00002797925],"domain_scores_gemma":[0.9998659,0.00001440539,0.00001463434,0.00005224395,0.00004577548,0.000007031322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006306404,0.00004130859,0.0006302506,0.0002118481,0.00004563478,0.00003851643,0.00006875686,0.03255903,0.05277418,0.006467438,0.004308883,0.9027911],"study_design_scores_gemma":[0.00003837125,0.0001497283,0.007989664,0.0001419518,0.00009313865,0.0006383887,0.0001322652,0.8039767,0.1118648,0.02825036,0.04659344,0.0001312359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00383505,0.0002648243,0.9899295,0.00003517688,0.00007030892,0.00003035124,0.0001946475,0.001647376,0.003992828],"genre_scores_gemma":[0.1561166,0.001220429,0.8284263,0.00008565527,0.00008684398,0.00008942349,0.001905453,0.0005573081,0.01151198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004440493,"threshold_uncertainty_score":0.01485491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462859459823187,"score_gpt":0.2320499296361045,"score_spread":0.2174213350378726,"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."}}