A Robust RGB-D Image-Based SLAM System

Visual SLAM is widely used in robotics and computer vision. Although there have been many excellent achievements over the past few decades, there are still some challenges. 2D feature-based SLAM algorithm has been suffering from the inaccurate or insufficient correspondences while dealing with the case of textureless or frequently repeating regions. Furthermore, most of the SLAM systems cannot be used for long-term localization in a wide range of environment because of the heavy burden of calculating and memory. In this paper, we propose a robust RGB-D keyframe-based SLAM algorithm. The novelty of proposed approach lies in using both 2D and 3D features for tracking, pose estimation and bundle adjustment. By using 2D and 3D features, the SLAM system can achieve high accuracy and robustness in some challenging environments. The experimental results on TUM RGB-D dataset [1] and ICL-NUIM dataset [2] verify the effectiveness of our algorithm.