Dense visual SLAM for RGB-D cameras

@article{Kerl2013DenseVS,
  title={Dense visual SLAM for RGB-D cameras},
  author={Christian Kerl and J{\"u}rgen Sturm and Daniel Cremers},
  journal={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year={2013},
  pages={2100-2106}
}
In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher pose accuracy. Furthermore, we propose an entropy-based similarity measure for keyframe selection and loop closure detection. From all successful matches, we build up a graph that we optimize using the g2o… 
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