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.
Kerl et al. REF proposed a dense direct RGB-D odometry by minimizing photometric error and depth error that leads to a higher pose accuracy when compared to sparse feature based arXiv:1711.05166v1 [cs.
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