Efficient relative pose estimation with an RGB-D camera in indoor environment

In this paper, we propose an efficient framework to estimate the motion of a robot when it is navigating indoors. Particularly, we mainly focus on the case where the robot is mounted with an RGB-D camera, and the environments have a lot of planar structures such as the floor, ceiling and walls. The novelties of our method lie in three-folds. First, an efficient normal vector extraction method for planes is proposed to fully make use of the planar structures. Without fitting planes, we only need to use the inverse-depth induced histograms to get the dominant planar structures as well as the normal of the planes. Second, since the robot is roaming indoors, we assume that the environment satisfies the weak Manhattan world constraint, i.e., the floor and ceiling are perpendicular to the walls. It is reasonable in our real world. Based on this assumption, we can calculate the relative camera motion by aligning the current local frame with respect to the world coordinate. Third, we present real-world RGB-D datasets that satisfy the weak Manhattan world constraint including 5,737 images, which make contributions to the community. Extensive experimental results show a very promising performance of our method in terms of accuracy, robustness and efficiency, especially in large-scale lowtextured scenes.