文献类型：会议论文

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]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

会议论文集：15th International Forum on Digital TV and Multimedia Communication (IFTC)

会议日期：SEP 20-21, 2018

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Visual SLAM; Semantic mapping; Atrous convolution; Real-time; Embedded system

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 x 212 on NVIDIA Jetson TX2 embedded module, which can be used in real-time visual SLAM.