Geometry Depth Consistency in RGBD Relative Pose Estimation
This work addresses the challenge of efficiently combining RGB and depth data for pose estimation in applications like robotics and AR, though it is incremental as it builds on existing RANSAC methods.
The paper tackles the problem of RGBD relative pose estimation by introducing a Geometric Depth Constraint (GDC) that restricts correspondences based on depth consistency, significantly reducing outliers and speeding up RANSAC, with experiments showing improved robustness and efficiency on datasets like TUM and ICL-NUIM.
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.