计算机应用与软件2018，Vol.35Issue(1)：183-190,8.DOI:10.3969/j.issn.1000-386x.2018.01.032
基于SLAM算法和深度神经网络的语义地图构建研究
RESEARCH ON SEMANTIC MAPPING BASED ON SLAM ALGORITHM AND DEEP NERUAL NETWORK
摘要
Abstract
Semantic mapping plays a key role in the task of mobile robot navigation.At present,the vision-based SLAM system has been able to achieve higher accuracy requirements,but the visual SLAM algorithm based on multi-view geometry cannot use the rich semantic information in space.The map point information reserved in the map is only spatial geometric point without semantic information.Since the algorithm based on convolution neural network has made a breakthrough in the field of object detection,the semantic mapping is constructed using the latest YOLO algorithm of object detection based on convolution neural network,which realized the real-time object detection in scene,combined with the SLAM algorithm.The proposed approach combines the accuracy of visual SLAM and the advantages of deep neural network in semantic extraction,which can improve the accuracy of SLAM algorithm,and the rich images collected can further help us to train deeper neural networks.This algorithm can be applied to the intelligent navigation of the robot,but also as a data collector for other visual tasks to collect the semantic and geometric information with the image data.关键词
语义地图/机器人定位与导航/目标检测/深度学习Key words
Semantic mapping/SLAM/Object detection/Deep learning分类
信息技术与安全科学引用本文复制引用
白云汉..基于SLAM算法和深度神经网络的语义地图构建研究[J].计算机应用与软件,2018,35(1):183-190,8.基金项目
上海市科委基础研究领域项目(14JC1402200). （14JC1402200）
