文献类型：期刊文献

英文题名：A high efficient 3D SLAM algorithm based on PCA

作者：Shi, Shangjie[1]; Zhou, Wei[1]; Liu, Shuang[1,2]

机构：[1] School of Mechanical and Powering, East China University of Science Technology, Shanghai, 200237, China; [2] Key Laboratory of Intelligent Perception, Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing, 210094, China

年份：2016

起止页码：109

外文期刊名：6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2016

收录：EI(收录号：20164302940146)

语种：英文

外文关键词：Robotics

摘要：In this paper, an improved algorithm for 3D SLAM is presented. A data dimensional reduction algorithm - Principal Component Analysis (PCA) is adopted, so as to speed up the rate of feature extraction from RGB-D images(640×480). To overcome the poor efficiency performance on the original RGB-D SLAM, a novel memory management algorithm and an incremental appearance-based loop closure detector are used to realize the 3D mapping and robot's trajectory estimation. In the back-end, the robot's trajectory and global map is optimized by g2o. The experiments demonstrate that the proposed method is faster and more effective than the original RGB-D SLAM. ? 2016 IEEE.