  In this paper a low-drift monocular SLAM method is proposed targeting indoor
scenarios, where monocular SLAM often fails due to the lack of textured
surfaces. Our approach decouples rotation and translation estimation of the
tracking process to reduce the long-term drift in indoor environments. In order
to take full advantage of the available geometric information in the scene,
surface normals are predicted by a convolutional neural network from each input
RGB image in real-time. First, a drift-free rotation is estimated based on
lines and surface normals using spherical mean-shift clustering, leveraging the
weak Manhattan World assumption. Then translation is computed from point and
line features. Finally, the estimated poses are refined with a map-to-frame
optimization strategy. The proposed method outperforms the state of the art on
common SLAM benchmarks such as ICL-NUIM and TUM RGB-D.
