Relative pose estimation for RGBD cameras is crucial in a number of
applications. Previous approaches either rely on the RGB aspect of the images
to estimate pose thus not fully making use of depth in the estimation process
or estimate pose from the 3D cloud of points that each image produces, thus not
making full use of RGB information. This paper shows that if one pair of
correspondences is hypothesized from the RGB-based ranked-ordered
correspondence list, then the space of remaining correspondences is restricted
to corresponding pairs of curves nested around the hypothesized correspondence,
implicitly capturing depth consistency. This simple Geometric Depth Constraint
(GDC) significantly reduces potential matches. In effect this becomes a filter
on possible correspondences that helps reduce the number of outliers and thus
expedites RANSAC significantly. As such, the same budget of time allows for
more RANSAC iterations and therefore additional robustness and a significant
speedup. In addition, the paper proposed a Nested RANSAC approach that also
speeds up the process, as shown through experiments on TUM, ICL-NUIM, and RGBD
Scenes v2 datasets.

该论文介绍了一种相对位姿估计方法，利用 RGBD 相机的 RGB 和深度信息，通过几何深度约束和嵌套 RANSAC 算法实现对相机位姿的快速准确估计。