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Abstract3D object classification is an important component in semantic scene understanding for mobile robots. However, many current systems do not consider the practical issues such as object representation from different… Click to show full abstract
Abstract3D object classification is an important component in semantic scene understanding for mobile robots. However, many current systems do not consider the practical issues such as object representation from different viewing positions of mobile robots. A novel 3D object representation is introduced using cylindrical occupancy grid and 3D convolutional neural network with row-wise max pooling layer. Due to the rotationally invariant characteristics of this method, robots can successfully classify 3D objects regardless of starting positions of object modelling. Experimental results on publicly available benchmark dataset show the significantly improved performance compared with other conventional algorithms.
Journal Title: International Journal of Control, Automation and Systems
Year Published: 2018
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