3D scene reconstruction is an important topic in computer vision. A complete scene is reconstructed from views acquired along camera trajectory, each view containing a small part of the scene. Tracking in textureless scenes is well known to be a Gordian knot of camera tracking, and how to obtain accurate 3D models quickly is a major challenge for existing systems. For the application of robotics, we propose a robust CPU-based approach to efficiently reconstruct indoor scene with a consumer RGB-D camera. The proposed approach bridges feature-based camera tracking and volumetric-based data integration together, and has a good reconstruction performance in terms of both robustness and efficiency. The key points in our approach include: 1) a robust and fast camera tracking method combining points and edges, which improves tracking stability in textureless scenes; 2) an efficient data fusion strategy to select camera views and integrate RGB-D images on multiple scales, which enhances the efficiency of volumetric integration; 3) a novel RGB-D scene reconstruction system, which can be quickly implemented on a standard CPU. Experimental results demonstrate that our approach reconstructs scenes with higher robustness and efficiency compared to state-of-the-art reconstruction systems.