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A Convolutional Learning System for Object Classification in 3-D Lidar Data

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1 Author(s)
Prokhorov, D. ; Toyota Res. Inst. NA, Ann Arbor, MI, USA

In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: (1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, (2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and (3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.

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Neural Networks, IEEE Transactions on  (Volume:21 ,  Issue: 5 )