文献类型：会议论文

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]East China Univ Sci & Technol, School Mech & Powering 1, Shanghai 200237, Peoples R China;[2]Nanjing Univ Sci & Technol, High Dimens Informat Minist Educ, Key Lab Intelligent Percept & Syst 2, Nanjing 210094, Jiangsu, Peoples R China

会议论文集：6th lEEE Annual International Conference on Cyber Technology in Automation, Control and Intelligent Systems (IEEE-CYBER)

会议日期：JUN 19-22, 2016

会议地点：Chengdu, PEOPLES R CHINA

语种：英文

外文关键词：3D SLAM; PCA and dimensional reduction

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(640x480). 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.