View-point Invariant 3D Classification for Mobile Robots Using a Convolutional Neural Network

被引：25

作者：

Moon, Jiyoun ^{[1
]}

Kim, Hanjun ^{[1
]}

Lee, Beomhee ^{[1
]}

机构：

[1] Seoul Natl Univ, Dept Elect Engn, Automat & Syst Res Inst, Seoul, South Korea

来源：

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2018年 / 16卷 / 06期

基金：

新加坡国家研究基金会;

关键词：

3D object classification; cylindrical CNN; mobile robots; view-point invariant; DESCRIPTORS;

D O I：

10.1007/s12555-018-0182-y

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

3D 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.

引用

页码：2888 / 2895

页数：8

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