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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