Simultaneous localization and mapping based on RGB-D images with filter processing and pose optimization

RGB-D camera can capture color and depth images simultaneously, and is widely used for simultaneous localization and mapping (SLAM) research. In this article, The RGB-D SLAM method was improved from two aspects. Firstly, the point cloud filter method was improved to more effectively decrease the noise and redundancy of RGB-D camera data; secondly, an ICP algorithm was used to improve the estimated accuracy of the pose transformation matrix and the trajectories of camera movement. The proposed RGB-D SLAM method was verified on public datasets. The experimental results demonstrate that our RGB-D SLAM method can effectively improve the accuracy of the autonomous positioning and mapping of robots.