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Motion planning is crucial for helping autonomous robots navigate complex environments efficiently. Recently, Augmented Reality (AR) has been introduced to improve Human-Robot Interaction (HRI) in mobile robot motion planning. However, AR gap-based reactive control systems often suffer from issues like sensor noise and inaccuracies, leading to higher levels of jerk and stress. On the other hand, Simultaneous Localization and Mapping (SLAM) provides a global understanding of the environment, ensuring robust navigation even in dynamic or unfamiliar areas. In this paper, we propose an AR-based Hector Simultaneous Localization and Mapping (SLAM) method for intuitive indoor mobile robot navigation that reduces jerk and stress. Our approach uses AR to set navigation goals and provide visual markers for the user, while SLAM ensures accurate real-time mapping for precise navigation and obstacle avoidance. For path planning, the robot uses Dijkstra's algorithm for global planning and Trajectory Rollout for local planning. We tested the effectiveness of our AR-based Hector SLAM in three different scenarios and compared the results with an admissible gap-based navigation algorithm. Experimental results showed that our method improved jerk and stress by 11.63% and 11.39% respectively, leading to smoother and safer trajectories.