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Real-time 3D point cloud obstacle discriminator apparatus and associated methodology for training a classifier via bootstrapping
Real-time 3D point cloud obstacle discriminator apparatus and associated methodology for training a classifier via bootstrapping
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机译:用于通过自举训练分类器的实时3D点云障碍识别器设备和相关方法
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摘要
Training a strong classifier by classifying point cloud data with a first classifier, inferring a first terrain map from the classified point cloud data, reclassifying the point cloud data with the first classifier based on the first terrain map, and training a second classifier based on the point cloud data reclassified with the first classifier based on the terrain map. The point cloud data is then classified with the second classifier, and the procedure followed with the first classifier is iteratively repeated until a strong classifier is determined. A strong classifier is determined when a probability of a terrain map matching a given terrain for the strong classifier is approximately equal to a probability of a terrain map matching the given terrain for a prior trained classifier.
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