A high efficient 3D SLAM algorithm based on PCA

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.