Fast Keyframe Selection and Switching for ICP-based Camera Pose Estimation

Simultaneous localization and mapping algorithms are important for high-quality registration used in augmented reality applications. Keyframe based SLAM can effectively reduce local drift by aligning a frame to the corresponding keyframe; however, it still suffers from losing trace for frames far from the keyframe. This work presents a fast keyframe selection and switching algorithm to replace unsuitable keyframes with qualified backup frames. The overhead of using backup process is greatly reduced by only inspecting the inlier information produced at the first iteration of iterative closest point (ICP) algorithm. Moreover, several useful criteria considering spatial and/or temporal relationships are also presented to evaluate the quality of keyframes and backup frames. Experimental results show that about 11.37% of relative pose error and 16.79% of the ICP iterations can be reduced by applying the proposed schemes as compared to the traditional keyframe-based approach. The reduction in computational time is achieved by speeding up the convergence of ICP, which is an additional benefit from applying the proposed schemes.