Abstract
In challenging environments, feature-based visual SLAM encounters frequent failures in frame tracking, introducing unknown poses to robotic applications. This paper introduces an immediate approach for recovering untracked camera poses. Through the retrieval of key information from elapsed untracked frames, lost poses are efficiently restored with a short time consumption. Taking account of reconstructed poses and map points during local optimizing, a denser local map is constructed around ambiguous frames to enhance the further SLAM procedure. The proposed method is implemented in a SLAM system, and monocular experiments are conducted on datasets. The experimental results demonstrate that our method can reconstruct the untracked frames in nearly real time, effectively complementing missing segments of the trajectory. Concurrently, the accuracy and robustness for subsequent tracking are improved through the integration of recovered poses and map points.