Abstract
SLAM-based occupancy grid maps are widely used for indoor mobile robots, but their noise, jagged obstacle borders, and spurious narrow gaps can mislead the classical grid-based path planners, e.g. A* algorithm, causing unstable routes, edge-hugging behavior, and increased computation. This manuscript proposes a lightweight pipeline to improve global path planning robustness and speed in warehouse-like environments. The approach first converts the SLAM map into a conservative binary representation and applies morphological refinement to remove small artifacts and smooth free-space connectivity. Next, the free space is skeletonized and transformed into a graph that captures the topology of navigable corridors. Global path planning is then performed using a hybrid strategy: a local grid search connects the start and goal regions to the skeleton, while the main route is computed on the skeleton graph. Experiments in two warehouse scenarios show that the proposed method substantially reduces node expansions and path planning time compared to full-grid A* algorithm, while producing more physically plausible paths that avoid false passages in noisy maps. These results indicate that morphological and skeleton-based global path planning can serve as a practical solution to enhance efficiency for robust navigation in realistic SLAM based path planning.