A convolutional learning system for object classification in 3-D lidar data

Authors:
Danil Prokhorov
Affiliations:
Toyota Research Institute NA, Ann Arbor, MI
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

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 ofthe 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.