Simultaneous Localization and Mapping (SLAM) is a key technology for robots to realize autonomous navigation in unknown environments. Aiming at the poor real-time performance and low accuracy of the commonly used RGB-Depth (RGB-D) SLAM system, a new RGB-D SLAM system was proposed to further improve the real-time performance and accuracy. Firstly, the Oriented FAST and Rotated BRIEF (ORB) algorithm was used to detect the image feature points, and the extracted feature points were processed by using the quadtree-based homogenization strategy, and the Bag of Words (BoW) was used to perform feature matching. Then, in the stage of system camera pose initial value estimation, an initial value which was closer to the optimal value was provided for back-end optimization by combining the Perspective n Point (P nP) and nonlinear optimization methods. In the back-end optimization, the Bundle Adjustment (BA) was used to optimize the initial value of the camera pose iteratively for obtaining the optimal value of the camera pose. Finally, according to the correspondence between the camera pose and the point cloud map of each frame, all the point cloud data were registered in a coordinate system to obtain the dense point cloud map of the scene, and the octree was used to compress the point cloud map recursively, so as to obtain a 3D map for robot navigation. On the TUM RGB-D dataset, the proposed RGB-D SLAM system, RGB-D SLAMv2 system and ORB-SLAM2 system were compared. Experimental results show that the proposed RGB-D SLAM system has better comprehensive performance on real-time and accuracy.