I have originally developed an indoor localization method called Visual-Geometric Matching (VGM), in which a line-segmented query image (visual) from an edge device is matched pixel-by-pixel with a line-segmented template image generated from an indoor building model (geometric) to determine the query image’s location. This method can be implemented as a server-client system, requiring only a monocular camera on the edge device, making it lightweight in terms of both weight and computational load. Through experiments, I have confirmed that a robot system integrating VGM with wheel odometry using an Extended Kalman Filter (EKF) successfully navigated an L-shaped corridor, ,repeatedly traveling back and forth for over an hour. The average localization error remained below 12 cm. The key advantage of this method is that it requires only a standard monocular camera for localization and does not rely on any additional physical infrastructure. This system holds great potential for various IoT applications.