  In the field of Simultaneous Localization and Mapping (SLAM), researchers
have always pursued better performance in terms of accuracy and time cost.
Traditional algorithms typically rely on fundamental geometric elements in
images to establish connections between frames. However, these elements suffer
from disadvantages such as uneven distribution and slow extraction. In
addition, geometry elements like lines have not been fully utilized in the
process of pose estimation. To address these challenges, we propose GFS-VO, a
grid-based RGB-D visual odometry algorithm that maximizes the utilization of
both point and line features. Our algorithm incorporates fast line extraction
and a stable line homogenization scheme to improve feature processing. To fully
leverage hidden elements in the scene, we introduce Manhattan Axes (MA) to
provide constraints between local map and current frame. Additionally, we have
designed an algorithm based on breadth-first search for extracting plane normal
vectors. To evaluate the performance of GFS-VO, we conducted extensive
experiments. The results demonstrate that our proposed algorithm exhibits
significant improvements in both time cost and accuracy compared to existing
approaches.
