Abstract-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 framework. We evaluated our approach extensively on publicly available benchmark datasets, and found that it performs well in scenes with low texture as well as low structure. In direct comparison to several stateof-the-art methods, our approach yields a significantly lower trajectory error. We release our software as open-source.
A dense RGB-D SLAM algorithm by minimizing both the photometric and the depth error over every pixels was proposed by Kerl et al. REF . And they selected keyframes and detected loop closure using entropy based similarity measurement, and optimized the pose graph in g2o framework.
Dense visual SLAM for RGB-D cameras
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science