  With the dominance of keyframe-based SLAM in the field of robotics, the
relative frame poses between keyframes have typically been sacrificed for a
faster algorithm to achieve online applications. However, those approaches can
become insufficient for applications that may require refined poses of all
frames, not just keyframes which are relatively sparse compared to all input
frames. This paper proposes a novel algorithm to correct the relative frames
between keyframes after the keyframes have been updated by a back-end
optimization process. The correction model is derived using conservation of the
measurement constraint between landmarks and the robot pose. The proposed
algorithm is designed to be easily integrable to existing keyframe-based SLAM
systems while exhibiting robust and accurate performance superior to existing
interpolation methods. The algorithm also requires low computational resources
and hence has a minimal burden on the whole SLAM pipeline. We provide the
evaluation of the proposed pose correction algorithm in comparison to existing
interpolation methods in various vector spaces, and our method has demonstrated
excellent accuracy in both KITTI and EuRoC datasets.
