文献类型：期刊文献

英文题名：Real-time semantic mapping of visual SLAM based on DCNN

作者：Chen, Xudong[1]; Zhu, Yu[1]; Zheng, Bingbing[1]; Huang, Junjian[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2019

卷号：1009

起止页码：194

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20192106962414)

语种：英文

外文关键词：Extraction - Semantics - Embedded systems - Motion planning - Feature extraction - Mapping

摘要：Visual SLAM (Simultaneous Localization and Mapping) has been widely used in location and path planning of unmanned systems. However, the map created by visual SLAM system only contain low-level information. The unmanned system can work better if high-level semantic information is included. In this paper, we proposed a visual semantic SLAM method using DCNN (Deep Convolution Neural Network). The network is composed of feature extraction, multi-scale process and classification layers. We apply atrous convolution to GoogLeNet for feature extraction to increase the speed of network and to increase the resolution of the feature map. Spatial pyramid pooling is used in multi-scale process and Softmax is used in classification layers. The results reveals that the mIoU of our network on PASCAL 2012 is 0.658 and it takes 101 ms to infer an image with the size of 256 × 212 on NVIDIA Jetson TX2 embedded module, which can be used in real-time visual SLAM. ? Springer Nature Singapore Pte Ltd 2019.