In view of the long time-consuming and poor precision of ＲGB-D SLAM algorithm in the field ofvisual SLAM，an implementation scheme of ＲGB-D SLAM visual odometry is proposed. Based on the traditional visual odometer implementation scheme，the OＲB feature extraction algorithm is used to extract the features of the image and calculate the descriptor to match the features of the map points in the local map. Finally，the PnP algorithm is used to calculate the camera pose. In order to ensure the accuracy of the tracking，it is proposed to use the triangulation algorithm to update the depth of the map points in the local map after calculating the camera pose. The experiment uses fr1 series data set in tum database to test. The experimental results show that the scheme can reduce the root-mean-square error in the tracking process by 9. 21% on average while ensuring real-time performance. The image quality of the point cloud map generated is also improved by 11. 21% .